mystic: scientific AI for automated real-time engineering design under uncertainty
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97 MATHEMATICS AND COMPUTING↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Cross-domain digital twin architecture for predictive maintenance via machine learning and Large Language Models
This research introduces a comprehensive framework for creating and deploying a digital twin platform for continuous monitoring and predictive maintenance within industrial settings. Through utilizing advanced technologies, including Unreal Engine 5, Unity 3D, the Message Queue Telemetry Transport protocol, Random Forest machine learning algorithms, and Large Language Models (LLMs), we establish a platform that digitally reproduces physical equipment and translates digital controls into real-world actions. This facilitates preventive maintenance approaches and improves operational effectiveness. The digital twin platform gathers sensor data from operational equipment, analyzes it using machine learning, and delivers practical insights to prevent potential malfunctions and enhance equipment performance. Furthermore, the incorporation of a web portal enables efficient monitoring and access to historical data, educational materials, and equipment status information. Preliminary findings indicate that digital twins can transform industrial equipment management and maintenance methodologies.
97 MATHEMATICS AND COMPUTING↗
An engineering treatise on the CARE II dual mode and coverage models
A mathematical model used to calculate the reliability of any dual-mode, spare-switching computer system was described, and some illustrative examples were presented. The possibility of extending the resultant computer program further was also examined, enabling it, in particular, to accommodate computer configurations involving more than two modes of operation.
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Connecting Minds: AI Use Cases to Bridge Power Systems and Large Language Models for Practical Applications
Recent advances in artificial intelligence (AI) and development of large language models (LLMs) present the opportunity to develop a new generation of power systems applications. In contrast with early power system AI applications based on structured numerical data, LLMs offer unique capabilities to perform logical reasoning using text documents, unstructured data, and application programming interface (API) calls to computational software. This paper seeks to bridge the knowledge gap between power systems engineers and LLM developers through a crosscutting explanation of use cases, characteristics, requirements, practical considerations from the perspectives of both LLM capabilities and industry needs. Specific focus is given to applications that can be realistically deployed by electric utilities. After introducing the architecture of LLMs and unique challenges of the power systems domain, this paper proposes twenty representative LLM applications grouped into categories of 1) power system operations, 2) asset management, 3) system planning and analytics, and 4) energy management and protection systems. Five use cases are presented within each category with descriptions of the motivation, objectives, approaches, example inputs / outputs, and benefits of each use case.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Prediction of Fatigue Crack Growth Using Regularized Numerical Models
Though it is known in the engineering community that successful analyses rest upon the proper balance of (1) theoretical analysis of mathematical models, (2) physical experimentation and (3) computational simulation, this balance is currently handled in sometimes unwieldy and inefficient manner. It is proposed to investigate and develop rigorous and computationally efficient method to effectively combine all available information, from both experimental measurements and mathematical models, in the emulation of physical systems. This will be specifically applied to fatigue crack growth in metallic structures of interest to NASA.
Meade, Andrew J.↗
Calculation Software
MathSoft Plus 5.0 is a calculation software package for electrical engineers and computer scientists who need advanced math functionality. It incorporates SmartMath, an expert system that determines a strategy for solving difficult mathematical problems. SmartMath was the result of the integration into Mathcad of CLIPS, a NASA-developed shell for creating expert systems. By using CLIPS, MathSoft, Inc. was able to save the time and money involved in writing the original program.
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Total Effective Dose from Radiologic Emissions from INL Facilities for Calculation of Population Dose for the INL 2024 Annual Site Environmental Report
Total effective radiation dose from airborne releases was calculated using air dispersion modeling performed by the National Oceanic and Atmospheric Administration (NOAA) Idaho Falls Office using their HYSPLIT computer model (Stein et al. 2015; Draxler et al. 2013), and the Dose Multi-Media (DOSEMM) dose assessment model (Rood 2019) . The objective of these calculations was to provide a grid of total effective dose across a model domain that encompasses a 50-mile (80-km) radius from any Idaho National Laboratory (INL) Site source. In addition to INL Site sources, releases from the Radiological and Environmental Sciences Laboratory (RESL) (IF-683) and IF-603 located at the INL Research Center (IRC) within the Idaho Falls city limits were also included. Due to tracking limitations, radionuclides released from IF-611 and IF-603 are modeled as released from IF-603. The dose results will be combined with GIS software to compute a total population dose for the calendar year (CY) 2024 and will be reported in the INL Annual Site Environmental Report (ASER). This report does not cover the population dose calculation and only documents generation of the gridded dose file.
42 - ENGINEERING↗
Transforming Energy Through Computational Excellence: A View From NREL
At the National Renewable Energy Laboratory (NREL)—a U.S. Department of Energy laboratory—computational science, high-performance computing, applied mathematics, advanced computer science, visualization, and data play a pivotal role in advancing energy abundance, affordability, security, and reliability. From fundamental scientifc discovery to systems engineering and analysis, NREL researchers tackle market-relevant challenges to develop solutions for an independent energy system that is reliable, resilient and secure. Collaborative partnerships with industry, government, and academia ensure that our research remains cutting edge, impactful, applicable, and aligned with real-world energy needs. This special issue of Computing in Science & Engineering highlights exemplary NREL projects where computational tools and methodologies drive discovery and accelerate innovation in scalable and integrated energy systems. The featured articles explore the role of computational modeling, high-performance computing, generative AI, and adaptive computing in advancing independent energy solutions, optimizing sustainability research, and enhancing decision-making for energy solutions using a broad mix of energy technologies. Here, these contributions demonstrate how NREL’s computational research bridges the gap between theoretical advancements and practical implementation, emphasizing interdisciplinary collaboration and a commitment to innovation, with a focus on translating computational excellence into real-world impact, thus accelerate progress toward national energy goals. By showcasing cutting-edge research at the intersection of computational science and energy systems, this issue aims to inspire and inform researchers, practitioners, and policymakers dedicated to shaping a more reliable energy future.
97 MATHEMATICS AND COMPUTING↗
Cyber-Informed Engineering for Strategic Planning
The CIE Guide for Engaging Organizational Leadership: Board and C-Suite describes how to apply CIE at the senior-level of organizational management. This guidance integrates CIE concepts into theory and practice of guiding coalition leadership to improve permeation of CIE concepts throughout organizational culture. This guide incorporates feedback from CIE community of practice volunteers in a study of how business administrative and strategic planning and management guidance and course materials can be applied to CIE implementation throughout an organization's principles and processes. This guide defines the interests guiding senior leadership, managers and supervisors, and technicians and workers involved in creating and operating and maintaining systems, and cultural/ historical/ political assumptions or influences shaped the landscape that could facilitate or impede development of CIE. The included guidance for organizational strategic management and leadership can be imbedded into contract guidance based on the CIE implementation guide and lessons learned from industry application.
97 MATHEMATICS AND COMPUTING↗
Challenges and Vision for Standardization of Biopolymer Data Sets for Machine Learning
Machine learning (ML) is transforming materials research, yet potential for biopolymer discovery remains constrained by fragmented data and nonstandardized reporting. Biopolymers differ significantly from synthetic polymers, requiring specialized approaches to represent their biosynthetic origins, hierarchical structures, and application-specific metrics. In this Perspective, we identify three core challenges limiting biopolymer representation: information encoding, data quality, and data sharing. We describe the most pressing issues and propose commensurate approaches to address each key challenge. Recommendations include the design and adoption of biopolymer-specific fingerprinting and representation frameworks, development of hybrid human-large language model (LLM) data extraction strategies, and expanding Findable, Accessible, Interoperable, Reusable (FAIR)-compliant repositories. We propose a robust foundation to define interoperable, high-quality data sets that capture the full context of biopolymer materials. Standardized metadata, shared ontologies, and community-driven infrastructure would enable scalable, reproducible workflows and accelerate the ML-driven development of biopolymers.
36 MATERIALS SCIENCE↗
Category-specific topological learning of metal–organic frameworks
Category-specific topological learning enables efficient and accurate prediction of various properties of metal–organic frameworks.
Gas selectivity↗
Preparing Underrepresented Minorities for STEM Careers in Energy
The purpose of the grant was to increase the participation of underrepresented minorities and women in STEM disciplines that are critical for the energy industry. The grant was implemented by the Fort Valley State University’s (FVSU) Cooperative Developmental Energy Program (CDEP). FVSU-CDEP enrolled two cohorts of six academically talented freshmen students into its accelerated five-year 3+2 dual degree STEM program. In the 3+2 dual degree format, students enroll at FVSU for three years and earn bachelor’s degrees in biology, chemistry, or mathematics and transfer to a partnering university for two years to earn second bachelor’s degrees in engineering, geology/geophysics, or health physics. FVSU-CDEP partnering universities consist of Georgia Tech, Grand Valley State University, Penn State University, University of Alabama, University of Arkansas, University of Nevada-Las Vegas, and the University of Texas-Austin. Cohort I enrolled at FVSU fall semester of 2017 and cohort II enrolled fall semester of 2020. All six of the students in cohort I earned bachelor’s STEM degrees at FVSU and transferred to a partnering university. Five of the six who transferred to a partnering university earned the second bachelor’s degree. One student transferred from a partnering university to a non-partner university to continue pursuit of the second degree. All students in cohort I had internships, attended professional meetings, and made presentations. Cohort II enrolled the fall-semester of 2020 in the midst of COVID epidemic. Cohort II was only funded for one year, 2020/2021. The DOE Office of Economic Impact and Diversity informed the PI that there would not be any funding for the fifth and final year (2021/2022) of the project. All six of the students in cohort II earned bachelor’s STEM degrees from FVSU and transferred to one of CDEP’s partnering universities. One of the students in cohort II graduate with an MS degree spring of 2023. Currently, four are pursuing graduate degrees at partnering universities and one withdrew from a partnering school because health reasons. Students in cohort II opportunities to participate in internship were negatively impact by the COVID epidemic (2020-2022). Of the 12 scholarship participants, 8 were females. Fifty percent of the males pursued second degrees in Engineering while only twenty-five percent of the females chose Engineering as their second degree.
42 ENGINEERING↗
Prompt Phrase Ordering Using Large Language Models in HPC: Evaluating Prompt Sensitivity
Large language models (LLMs) have demonstrated effective performance in domain-specific tasks, often requiring a well-designed prompt to guide their responses. However, optimizing the right prompt is challenging due to prompt sensitivity—the phenomenon where small changes in the prompt can lead to significant variations in performance. In this study, we evaluate prompt performance by examining all permutations of independent phrases to investigate prompt sensitivity and robustness. We used two datasets: the GSM8k dataset, which assesses mathematical reasoning, and a custom template prompt for summarizing database metadata. Our goal was to evaluate the performance across all permutations of a sequence of prompt phrases. The study was conducted using the llama3-instruct- 7B model hosted on Ollama, with computations parallelized in a high-performance computing environment. By comparing the average index of phrases in the best and worst-performing prompts, we found that the order of independent phrases within a prompt significantly impacts LLM performance. Additionally, we used Hamming distance to assess changes between phrase orderings, concluding that prompt modifications can dramatically affect scores, often by almost random chance. These findings support existing research on prompt sensitivity. We discuss the challenges of prompt optimization, noting that altering phrases in a successful prompt does not always result in another successful prompt.
97 MATHEMATICS AND COMPUTING↗
The role of under-determined approximations in engineering and science application
There is currently a great deal of interest in using response surfaces in the optimization of aircraft performance. The objective function and/or constraint equations involved in these optimization problems may come from numerous disciplines such as structures, aerodynamics, environmental engineering, etc. In each of these disciplines, the mathematical complexity of the governing equations usually dictates that numerical results be obtained from large computer programs such as a finite element method program. Thus, when performing optimization studies, response surfaces are a convenient way of transferring information from the various disciplines to the optimization algorithm as opposed to bringing all the sundry computer programs together in a massive computer code. Response surfaces offer another advantage in the optimization of aircraft structures. A characteristic of these types of optimization problems is that evaluation of the objective function and response equations (referred to as a functional evaluation) can be very expensive in a computational sense. Because of the computational expense in obtaining functional evaluations, the present study was undertaken to investigate under-determinined approximations. An under-determined approximation is one in which there are fewer training pairs (pieces of information about a function) than there are undetermined parameters (coefficients or weights) associated with the approximation. Both polynomial approximations and neural net approximations were examined. Three main example problems were investigated: (1) a function of one design variable was considered; (2) a function of two design variables was considered; and (3) a 35 bar truss with 4 design variables was considered.
Carpenter, William C.↗
Effect of design selection on response surface performance
The mathematical formulation of the engineering optimization problem is given. Evaluation of the objective function and constraint equations can be very expensive in a computational sense. Thus, it is desirable to use as few evaluations as possible in obtaining its solution. In solving the equation, one approach is to develop approximations to the objective function and/or restraint equations and then to solve the equation using the approximations in place of the original functions. These approximations are referred to as response surfaces. The desirability of using response surfaces depends upon the number of functional evaluations required to build the response surfaces compared to the number required in the direct solution of the equation without approximations. The present study is concerned with evaluating the performance of response surfaces so that a decision can be made as to their effectiveness in optimization applications. In particular, this study focuses on how the quality of approximations is effected by design selection. Polynomial approximations and neural net approximations are considered.
Carpenter, William C.↗
Techniques for modeling the reliability of fault-tolerant systems with the Markov state-space approach
This paper presents a step-by-step tutorial of the methods and the tools that were used for the reliability analysis of fault-tolerant systems. The approach used in this paper is the Markov (or semi-Markov) state-space method. The paper is intended for design engineers with a basic understanding of computer architecture and fault tolerance, but little knowledge of reliability modeling. The representation of architectural features in mathematical models is emphasized. This paper does not present details of the mathematical solution of complex reliability models. Instead, it describes the use of several recently developed computer programs SURE, ASSIST, STEM, and PAWS that automate the generation and the solution of these models.
Butler, Ricky W.↗
Uncertainty Propagation from Experiment Measurements to Modeling Approaches: A Case for SMR Steam Entrainment Testing
To license new and advanced reactor designs, regulators must be convinced that their unique safety cases—relative to existing large scale reactors—have been adequately addressed by the designed reactor protection systems. In water cooled small modular reactors (SMRs), droplet entrainment in steam flow has significant implications on the progression of accident scenarios due to its compact design features, which requires representative test data applicable to SMR designs. Computer code, modeling and simulation (M&S) tools and models require adequate verification, assessment, and qualification. This includes M&S results validation against scaled empirical data within allowable uncertainty bands to gain regulatory approvals during the various stages of reactor system design, demonstration, and commercialization. However, measurement uncertainty within the empirical datasets and test data applicability ranges requires careful consideration of M&S inputs (i.e., boundary conditions, and initial conditions), and verification and validation efforts. This study focuses on uncertainty quantification in designing scaled test facilities for SMR applications with appropriate measurements and a standard data-reduction method to estimate thermal hydraulics characteristics parameters that incorporate physics phenomena of interest. In addition, this study supports the evaluation model development and assessment process using M&S that interfaces with advanced computing tools and digital twin capabilities. This will allow synchronization between experiment and modeling approaches for droplet entrainment testing and analysis, improving diagnostics, prognostics, and decision-making to accelerate regulatory approval.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
High-order limiting methods using maximum principle bounds derived from the Boltzmann equation I: Euler equations
The use of limiting methods for high-order numerical approximations of hyperbolic conservation laws generally requires defining an admissible region/bounds for the solution. In this work, we present a novel approach for computing solution bounds and limiting for the Euler equations through the kinetic representation provided by the Boltzmann equation, which allows for extending limiters designed for linear advection directly to the Euler equations. Given an arbitrary set of solution values to compute bounds over (e.g., numerical stencil) and a desired linear advection limiter, the proposed approach yields an analytic expression for the admissible region of particle distribution function values, which may be numerically integrated to yield a set of bounds for the density, momentum, and total energy. Further, these solution bounds are shown to preserve positivity of density/pressure/internal energy and, when paired with a limiting technique, can robustly resolve strong discontinuities while recovering high-order accuracy in smooth regions without any ad hoc corrections (e.g., relaxing the bounds). This approach is demonstrated in the context of an explicit unstructured high-order discontinuous Galerkin/flux reconstruction scheme for a variety of difficult problems in gas dynamics, including cases with extreme shocks and shock-vortex interactions. Furthermore, this work presents a foundation for limiting techniques for more complex macroscopic governing equations that can be derived from an underlying kinetic representation for which admissible solution bounds are not well-understood.
42 ENGINEERING↗