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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Status of HEU-Pb in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook

The Department of Energy invests tens of millions of dollars each year to develop the next generation of nuclear engineering modeling & simulation (M&S) tools. These tools are used to analyze advanced reactor designs and the safety of current nuclear operations. As computers become more powerful, we are able to enhance resolution in our calculations. This improved resolution is taking us to a point where the limitations of simulation capability are in the quality of data, including our ability to quantify the uncertainty and sensitivity of the data. In order to model systems of interest with increasing accuracy, the industry must improve key nuclear data measurements. Thus, M&S tools need evaluated and quality-assured experimental data for validation purposes. The International Criticality Safety Benchmark Evaluation Project (ICSBEP) compiles and evaluates experiment data in a handbook that can be used by criticality safety engineers and others to validate computer codes and cross-section libraries at nuclear facilities. Both critical and subcritical experiments are included in the handbook. Figure 1 organizes all the benchmark evaluations that have been performed by the isotope of interest, in this case Pb, and the average neutron energy the system. Compared to other isotopes of interest for nuclear applications, there are few benchmark evaluations for Pb systems. The lack of integral measurements to determine errors in Pb cross-section data has caused the latest nuclear cross-section libraries to over/underestimate k eff compared to experimental results. Therefore, this evaluation fills an important knowledge gap in benchmark evaluations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Improvements to CTF Code Verification and Unit Testing (FY2020)

In 2010, the U.S. Department of Energy created its first Energy Innovation Hub, which focuses on improving Light Water Reactors (LWRs) through Modeling and Simulation. This hub, named the Consortium for the Advanced Simulation of LWRs (CASL), attempts to characterize and understand LWR behavior under normal operating conditions and use any gained insights to improve their efficiency. In collaboration with North Carolina State University (NCSU), CASL has worked extensively on the thermal-hydraulic subchannel code Coolant Boiling in Rod Arrays—Three Field (COBRA-TF). The NCSU/CASL version of COBRA-TF has been rebranded as CTF. This document focuses on code verification test problems that ensure CTF converges to the correct answer for the intended application. The suite of code verification tests are mapped to the underlying conservation equations of CTF, and significant gaps are addressed. Convergence behavior and numerical errors are quantified for each of the tests. Tests that converge at the correct rate to the corresponding analytic solution are incorporated into the CTF automated regression suite. A new verification utility is created for this purpose, which enables code verification by generalizing the process. For problems that do not behave correctly, the results are reported but the problem is not included in the regression suite. In addition to verification studies, this document also quantifies the existing tests of constitutive models. A few existing gaps are addressed by adding new unit tests.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

FY20 Improvements to CTF Code Verification and Unit Testing

In 2010, the U.S. Department of Energy created its first Energy Innovation Hub, which focuses on improving Light Water Reactors (LWRs) through Modeling and Simulation. This hub, named the Consortium for the Advanced Simulation of LWRs (CASL), attempts to characterize and understand LWR behavior under normal operating conditions and use any gained insights to improve their efficiency. In collaboration with North Carolina State University (NCSU), CASL has worked extensively on the thermal-hydraulic subchannel code Coolant Boiling in Rod Arrays–Three Field (COBRA-TF). The NCSU/CASL version of COBRA-TF has been rebranded as CTF. This document focuses on code verification test problems that ensure CTF converges to the correct answer for the intended application. The suite of code verification tests are mapped to the underlying conservation equations of CTF, and significant gaps are addressed. Convergence behavior and numerical errors are quantified for each of the tests. Tests that converge at the correct rate to the corresponding analytic solution are incorporated into the CTF automated regression suite. A new verification utility is created for this purpose, which enables code verification by generalizing the process. For problems that do not behave correctly, the results are reported but the problem is not included in the regression suite. In addition to verification studies, this document also quantifies the existing tests of constitutive models. A few existing gaps are addressed by adding new unit tests.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2012

Spatial Variability in Column CO2 Inferred from High Resolution GEOS-5 Global Model Simulations: Implications for Remote Sensing and Inversions

Column CO2 observations from current and future remote sensing missions represent a major advancement in our understanding of the carbon cycle and are expected to help constrain source and sink distributions. However, data assimilation and inversion methods are challenged by the difference in scale of models and observations. OCO-2 footprints represent an area of several square kilometers while NASA s future ASCENDS lidar mission is likely to have an even smaller footprint. In contrast, the resolution of models used in global inversions are typically hundreds of kilometers wide and often cover areas that include combinations of land, ocean and coastal areas and areas of significant topographic, land cover, and population density variations. To improve understanding of scales of atmospheric CO2 variability and representativeness of satellite observations, we will present results from a global, 10-km simulation of meteorology and atmospheric CO2 distributions performed using NASA s GEOS-5 general circulation model. This resolution, typical of mesoscale atmospheric models, represents an order of magnitude increase in resolution over typical global simulations of atmospheric composition allowing new insight into small scale CO2 variations across a wide range of surface flux and meteorological conditions. The simulation includes high resolution flux datasets provided by NASA s Carbon Monitoring System Flux Pilot Project at half degree resolution that have been down-scaled to 10-km using remote sensing datasets. Probability distribution functions are calculated over larger areas more typical of global models (100-400 km) to characterize subgrid-scale variability in these models. Particular emphasis is placed on coastal regions and regions containing megacities and fires to evaluate the ability of coarse resolution models to represent these small scale features. Additionally, model output are sampled using averaging kernels characteristic of OCO-2 and ASCENDS measurement concepts to create realistic pseudo-datasets. Pseudo-data are averaged over coarse model grid cell areas to better understand the ability of measurements to characterize CO2 distributions and spatial gradients on both short (daily to weekly) and long (monthly to seasonal) time scales

Ott, L.↗
DOE OSTI2020

Sandia Electromagnetic Environment Simulator Facilities

Sandia National Laboratories’ (Sandia’s) Electrical Sciences Group (1350) provides a spectrum of solutions for electromagnetic (EM) environment effects on electrical systems to advance physical understanding including experiments, analytical and numerical model development leveraging first-principles physics, and high-performance computational modeling and simulation (M&S). The six departments in the Electrical Sciences Group provide analysis, design guidance, and experiments in support of nuclear weapons qualification in normal, abnormal, and hostile environments, as well as research and development for advanced electrical systems that can operate through these environments. This document is intended to provide a quick summary of Sandia’s EM experimental facilities, most of which provide unique national capabilities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗
NASA NTRS2010

Toward GEOS-6, A Global Cloud System Resolving Atmospheric Model

NASA is committed to observing and understanding the weather and climate of our home planet through the use of multi-scale modeling systems and space-based observations. Global climate models have evolved to take advantage of the influx of multi- and many-core computing technologies and the availability of large clusters of multi-core microprocessors. GEOS-6 is a next-generation cloud system resolving atmospheric model that will place NASA at the forefront of scientific exploration of our atmosphere and climate. Model simulations with GEOS-6 will produce a realistic representation of our atmosphere on the scale of typical satellite observations, bringing a visual comprehension of model results to a new level among the climate enthusiasts. In preparation for GEOS-6, the agency's flagship Earth System Modeling Framework [JDl] has been enhanced to support cutting-edge high-resolution global climate and weather simulations. Improvements include a cubed-sphere grid that exposes parallelism; a non-hydrostatic finite volume dynamical core, and algorithm designed for co-processor technologies, among others. GEOS-6 represents a fundamental advancement in the capability of global Earth system models. The ability to directly compare global simulations at the resolution of spaceborne satellite images will lead to algorithm improvements and better utilization of space-based observations within the GOES data assimilation system

Putman, William M.↗
NASA NTRS1977

Terminal area air traffic control simulation

To study the impact of advanced aeronautical technologies on operations to and from terminal airports, a computer model of air traffic movements was developed. The advantages of fast-time simulation are discussed, and the arrival scheduling and flight simulation are described. A New York area study, user's guide, and programmer's guide are included.

Source record↗
DOE OSTI2015

Generalized and efficient algorithm for computing multipole energies and gradients based on Cartesian tensors

Accurate representation of intermolecular forces has been the central task of classical atomic simulations, known as molecular mechanics. Recent advancements in molecular mechanics models have put forward the explicit representation of permanent and/or induced electric multipole (EMP) moments. The formulas developed so far to calculate EMP interactions tend to have complicated expressions, especially in Cartesian coordinates, which can only be applied to a specific kernel potential function. For example, one needs to develop a new formula each time a new kernel function is encountered. The complication of these formalisms arises from an intriguing and yet obscured mathematical relation between the kernel functions and the gradient operators. Here, I uncover this relation via rigorous derivation and find that the formula to calculate EMP interactions is basically invariant to the potential kernel functions as long as they are of the form f(r), i.e., any Green’s function that depends on inter-particle distance. I provide an algorithm for efficient evaluation of EMP interaction energies, forces, and torques for any kernel f(r) up to any arbitrary rank of EMP moments in Cartesian coordinates. The working equations of this algorithm are essentially the same for any kernel f(r). Recently, a few recursive algorithms were proposed to calculate EMP interactions. Depending on the kernel functions, the algorithm here is about 4–16 times faster than these algorithms in terms of the required number of floating point operations and is much more memory efficient. I show that it is even faster than a theoretically ideal recursion scheme, i.e., one that requires 1 floating point multiplication and 1 addition per recursion step. This algorithm has a compact vector-based expression that is optimal for computer programming. The Cartesian nature of this algorithm makes it fit easily into modern molecular simulation packages as compared with spherical coordinate-based algorithms. A software library based on this algorithm has been implemented in C++11 and has been released.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

EnergyPlus Model Context Protocol Server (EnergyPlus-MCP) v0.1

EnergyPlus-MCP is the first open-source Model Context Protocol server specifically designed for EnergyPlus building energy simulation. This innovative software enables AI assistants and other applications to interact programmatically with EnergyPlus through a standardized, secure interface, eliminating traditional technical barriers in building energy modeling. The software provides specialized tools across five functional domains: server management, model configuration and loading, comprehensive building component inspection, systematic model modification, and simulation execution with results visualization. Key features include automated HVAC system discovery and topology mapping, advanced schedule analysis, intelligent model validation, and interactive visualization capabilities. EnergyPlus-MCP's layered architecture ensures robust separation between protocol communication and domain expertise, enabling scalable deployment across organizations, educational institutions, and research teams. Unlike direct LLM approaches that suffer from inconsistent results and security gaps, EnergyPlus-MCP provides validated, reliable interactions while maintaining scientific rigor. This democratizes sophisticated building energy analysis, making EnergyPlus accessible to broader audiences through conversational interfaces and streamlined workflows.

Li, Han [Lawrence Berkeley National Laboratory (LB↗
DOE OSTI2024

Increased accuracy of multiphysics simulations through flexible execution, transient algorithms, and modular physics

The MOOSE framework is a foundational capability used by the NEAMS program to create over 15 different simulation tools for advanced nuclear reactors. Due to MOOSE’s broad use, improvements to the framework in support of modeling and simulation goals are critical to the program. Such improvements can take many forms, including optimization, improved user experience, streamlined application programming interfaces (APIs), parallelism, and new capabilities. The work described in this report was conducted in direct support of the simulation tools and has already been deployed. The capabilities were implemented in the same order as they are covered in this report: multiple time integrators in the same input file, initial design of framework Components, an input file Application block, extension of NetGen to 3D geometries in MOOSE, and deployment of executors in the multi-system paradigm. These five additions are fundamental capabilities that will be leveraged by many NEAMS applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Improvements to MOOSE user workflow through polyhedral elements, automation, and concise physics syntax

The MOOSE framework is a foundational capability used by the NEAMS program to create over 15 different simulation tools for advanced nuclear reactors. Due to MOOSE's broad use, improvements to the framework in support of modeling and simulation goals are critical to the program. Such improvements can take many forms, including optimization, improved user experience, streamlined application programming interfaces (APIs), parallelism, and new capabilities. The work described in this report was conducted in direct support of NEAMS tools and includes: addition of support for polyhedral elements, incorporation of mesh smoothers for mesh repair, integration of the Physics and ActionComponents systems, expansion of the Convergence system, and exploration of automated input file generation. These five areas of development are fundamental capabilities that will be leveraged by many NEAMS applications.

97 - MATHEMATICS AND COMPUTING↗
NASA NTRS2016

Advanced Control Considerations for Turbofan Engine Design

This paper covers the application of a model-based engine control (MBEC) methodology featuring a self tuning on-board model for an aircraft turbofan engine simulation. The nonlinear engine model is capable of modeling realistic engine performance, allowing for a verification of the advanced control methodology over a wide range of operating points and life cycle conditions. The on-board model is a piece-wise linear model derived from the nonlinear engine model and updated using an optimal tuner Kalman Filter estimation routine, which enables the on-board model to self-tune to account for engine performance variations. MBEC is used here to show how advanced control architectures can improve efficiency during the design phase of a turbofan engine by reducing conservative operability margins. The operability margins that can be reduced, such as stall margin, can expand the engine design space and offer potential for efficiency improvements. Application of MBEC architecture to a nonlinear engine simulation is shown to reduce the thrust specific fuel consumption by approximately 1% over the baseline design, while maintaining safe operation of the engine across the flight envelope.

control↗
NASA NTRS1987

Simulation of automatic precision departures and missed approaches using the microwave landing system

Results of simulated precision departures and missed approaches using MLS guidance concepts are presented. The study was conducted under the Terminal Configured Vehicle (TCV) Program, and is an extension of previous work by DAC under the Advanced Transport Operating System (ATOPS) Technology Studies Program. The study model included simulation of an MD-80 aircraft, an autopilot, and a MLS guidance computer that provided lateral and vertical steering commands. Precision departures were evaluated using a noise abatement procedure. Several curved path departures were simulated with MLS noise and under various environmental conditions. Missed approaches were considered for the same runway, where lateral MLS guidance maintained the aircraft along the extended runway centerline. In both the departures and the missed approach cases, pitch autopilot takeoff and go-around modes of operation were used in conjunction with MLS lateral guidance.

Feather, J. B.↗
DOE OSTI2018

Advancing graph-based algorithms for predicting flow and transport in fractured rock

Discrete fracture network (DFN) models are a powerful alternative to continuum models for subsurface flow and transport simulations because they explicitly include fracture geometry and network topology, thereby allowing for better characterization of the latter's influence on flow and transport through fractured media. Recent advances in high performance computing have opened the door for flow and transport simulations in large explicit three-dimensional DFN, but this increase in model fidelity and system size comes at a huge computational cost because of the large number of mesh elements required to represent thousands of fractures (with sizes that can range several orders of magnitude, from mm to km). In most subsurface applications, fracture characteristics are only known statistically and numerous realizations are needed to bound uncertainty in flow and transport in the system, thereby exacerbating the computational burden. blackGraphs provide a simple and elegant way to characterize, query, and interrogate fracture network connectivity. We propose a DFN model reduction framework where various graph-representations are used in conjunction with high-fidelity DFN simulations to increase computational efficiency while retaining accuracy of key quantities of interest. The appropriate choice of a graph-representation, namely, which attributes of the DFN are to be represented as nodes and which ones as edges connecting those nodes, depends on the relevant scientific questions. We demonstrate that the proposed DFN model reduction framework provides an efficient means for DFN modeling through both system reduction of the DFN using graph-based properties and combining DFN and graph-based flow and transport simulations.

58 GEOSCIENCES↗
NASA NTRS1991

Infiltration/cure modeling of resin transfer molded composite materials using advanced fiber architectures

A model was developed which can be used to simulate infiltration and cure of textile composites by resin transfer molding. Fabric preforms were resin infiltrated and cured using model generated optimized one-step infiltration/cure protocols. Frequency dependent electromagnetic sensing (FDEMS) was used to monitor in situ resin infiltration and cure during processing. FDEMS measurements of infiltration time, resin viscosity, and resin degree of cure agreed well with values predicted by the simulation model. Textile composites fabricated using a one-step infiltration/cure procedure were uniformly resin impregnated and void free. Fiber volume fraction measurements by the resin digestion method compared well with values predicted using the model.

Loos, Alfred C.↗
DOE OSTI2016

The MELCOR Analysis of Chinshan Nuclear Power Plant Spent Fuel Pool for Fukushima-like Accident

The safety analysis of the nuclear power plant (NPP) is very important work in the NPP safety. Especially after the Fukushima NPP accident occurred, the importance of NPP safety analysis has been raised and there is more concern for the safety of NPPs in Taiwan. Because the earthquake and tsunami occurred, the cooling system of spent fuel pool failed and the safety issue of spent fuel pool generated in Japan's Fukushima NPP. Chinshan NPP is the first NPP in Taiwan which is BWR/4 plant and OLTP (Original Licensed Thermal Power) for each unit is 1775 MWt. Chinshan NPP finished SPU (stretch power uprate) and the operating power is 103.66% of the OLTP, which is 1840 MWt now. After Fukushima NPP accident occurred, in order to concern the safety of Chinshan NPP spent fuel pool, we performed the safety analysis of spent fuel pool by using MELCOR/SNAP which also assumed the cooling system of spent fuel pool failed. The geometry of Chinshan NPP spent fuel pool is 12.17 m x 7.87 m x 11.61 m and the initial condition is 60 deg. (water temperature) / 1.013 x 10{sup 5} Pa. And, the total power of the fuels is roughly 8.9 MWt initially. MELCOR is developed by Sandia National Laboratories. MELCOR is a fully integrated, engineering-level computer code that models the progression of severe accidents in light water reactor NPPs, including containment response and leakage of activity to the environment. A broad spectrum of severe accident phenomena in both BWRs and PWRs is treated in MELCOR in a unified framework. These include thermalhydraulic response in the reactor coolant system, reactor cavity, containment, and confinement buildings; core heatup, degradation, and relocation; core-concrete attack; hydrogen production, transport, and combustion; fission product release and transport behavior. Current uses of MELCOR include estimation of severe accident source terms and their sensitivities and uncertainties in a variety of applications. According to Dr. Carbajo's paper, MELCOR was used to perform the study of spent fuel pool of Fukushima Daiichi Unit 4. Niina E. Koenoenen used MELCOR to establish the spent fuel pool model of a Nordic BWR. The loss-of-pool-cooling accidents was simulated and analyzed by this model. The above studies indicate that MELCOR is capable of handling the simulation of the spent fuel pool. An increasing number of researchers are using MELCOR code to analyze test facilities and nuclear power plants. Jun Wang et al. used MELCOR to perform core degradation simulation and MELCOR results were compared with CORA experimental data. Through this work, they reviewed the performance of MELCOR COR package in detail. Tae Woon Kim et al. developed MELCOR model of APR1400 (an advanced pressurized water reactor). They used this model to perform the LBLOCA transient simulation and sensitivity study. Longze Li et al. established the MELCOR model of CPR1000 (a Chinese pressurized reactor 1000-MW power plant). The station blackout (SBO) with failure of the steam generator (SG) safety relief valve (SRV) transient was simulated and analyzed by this model. SNAP is a graphic user interface program that processes the inputs and outputs of MELCOR. In addition, there is an animation function in SNAP which can present the animation of analysis results. There are two main steps in this research. The first step was the establishment of Chinshan NPP spent fuel pool MELCOR/SNAP model. And the transient analysis of Chinshan NPP spent fuel pool MELCOR/SNAP model under the cooling system failure condition was performed. The next step was the comparison among the MELCOR, TRACE and CFD data. TRACE and CFD data were from INER reports. In addition, the animation model of Chinshan NPP spent fuel pool was presented by using the animation function of SNAP with MELCOR/SNAP analysis results. This study has developed MELCOR/SNAP model of Chinshan NPP spent fuel pool successfully. By using the above model, the safety analysis of the spent fuel pool was performed under the cooling system of spent fuel pool failed condition. The analysis results of MELCOR, TRACE and CFD were similar in this case. It indicated that there was a respectable accuracy in MELCOR/SNAP model. The analysis results depicted that the uncovered of the fuels occurred at 2.6 day and the metal-water reaction of fuels occurred roughly at 3.7 day after the cooling system failed. The above results indicated that the failure of cladding occurred after 3.7 day. This study's results can help to evaluate the safety issue of Chinshan NPP spent fuel pool. In addition, the cooling system and some mitigation measures of spent fuel pool is not simulated in the above model. However, we will do it in the future. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Cyote-attack Chain Estimator

Attack Chain Estimator (ACE) Application Overview The Attack Chain Estimator (ACE) Application is a sophisticated tool designed for the ingestion, classification, sequencing, and enrichment of cybersecurity threat reports. This application leverages advanced machine learning models and extensive historical data to provide comprehensive insights into cyber threats, specifically targeting Industrial Control Systems (ICS). Purpose The primary functions of the ACE Application include: Ingestion of Cybersecurity Threat Reporting: Capable of ingesting text-based threat reports in markdown or text file format. Supports ingestion of structured data from other sources in STIX/JSON format. Classification of Report’s Text-Based Events: Utilizes a DeBERTa classifier, specifically trained on cybersecurity data, to map the events to MITRE ATT&CK for ICS Tactics and Techniques. Classification is performed using multiple Jupyter notebooks and machine learning workflows hosted as FastAPI microservices: regex_data deberta_base_35_train_hft_classifier_mlflow.ipynb hft_regex_classifier_mlflow.ipynb param_train_hft_classifier_mlflow.ipynb regex_tactic_tech.ipynb Ordering of Tactics, Techniques, and Observable Events: Sequences the identified tactics, techniques, and events to form a coherent attack chain. Enrichment with Historical Attack Chain Details: Enhances the attack chain with details from historical attacks using a Markov model developed from CyOTE Precursor Analysis Report data. The Markov model is available as a FastAPI endpoint for seamless integration. Enrichment with Adversary Emulation Capabilities Data: Integrates adversary emulation capabilities data using MITRE Caldera for OT adversary abilities UUIDs. Export of Output Files: Provides options to export the enriched attack chain in JSON or CSV formats. Routing of Output to Other Applications: Facilitates routing of output to various platforms and applications, including: Threat Intelligence Platforms COREII Scout for Threat Intelligence Analysis COREII Modeling and Simulation for Adversary Emulation Technical Description The ACE Application is an advanced cybersecurity tool designed to provide detailed threat analysis and sequence generation. It is built on a robust architecture that integrates natural language processing, machine learning, and historical data modeling. Key Components: Data Ingestion Module: Handles the input of threat reports and data from various formats, ensuring flexibility in data sources. Classification Engine: Employs DeBERTa-based classifiers hosted as FastAPI microservices to analyze and classify threat report events in accordance with the MITRE ATT&CK framework for ICS. Sequence Generator: Orders the classified events into a logical attack chain, providing clear insight into the sequence of tactics and techniques used in the threat. Enrichment Engine: Integrates historical data and adversary emulation capabilities to enhance the attack chain with valuable context and additional details. The historical data enrichment is powered by a Markov model, which is available as a FastAPI endpoint. Export and Routing Module: Facilitates the export of the enriched attack chain in multiple formats and routes the output to designated applications for further analysis or emulation.

Paul, Tony [Idaho National Laboratory (INL), Idaho↗