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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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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

WE-G-BRA-06: Application of Systems and Control Theory-Based Hazard Analysis to Radiotherapy

Purpose: The process of delivering radiation occurs in a complex socio-technical system heavily reliant on human operators. Furthermore, both humans and software are notoriously challenging to account for in traditional hazard analysis models. High reliability industries such as aviation have approached this problem through using hazard analysis techniques grounded in systems and control theory. The purpose of this work is to apply the Systems Theoretic Accident Model Processes (STAMP) hazard model to radiotherapy. In particular, the System-Theoretic Process Analysis (STPA) approach is used to perform a hazard analysis of a proposed on-line adaptive cranial radiosurgery procedure that omits the CT Simulation step and uses only CBCT for planning, localization, and treatment. Methods: The STPA procedure first requires the definition of high-level accidents and hazards leading to those accidents. From there, hierarchical control structures were created followed by the identification and description of control actions for each control structure. Utilizing these control structures, unsafe states of each control action were created. Scenarios contributing to unsafe control action states were then identified and translated into system requirements to constrain process behavior within safe boundaries. Results: Ten control structures were created for this new CBCT-only process which covered the areas of hospital and department management, treatment design and delivery, and vendor service. Twenty three control actions were identified that contributed to over 80 unsafe states of those control actions resulting in over 220 failure scenarios. Conclusion: The interaction of people, hardware, and software are highlighted through the STPA approach. STPA provides a hierarchical model for understanding the role of management decisions in impacting system safety so that a process design requirement can be traced back to the hazard and accident that it is intended to mitigate. Varian Medical Systems, Inc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Digital Twin for Chemical Science (DTCS) v0.01

Directly visualizing the trajectories of chemistry can unravel novel insights into the behavior of catalysts, gas phase reactions, photo-induced dynamics, and building blocks for quantum information processing. The ability of explicitly identifying, tracking, and tagging the exchange of matter, hence the annihilation and creation of new chemical species, can be best realized through a close coupling of theory and experiment. While the synchrotron-based characterization facilities propelled rapidly in its hardware, providing higher brightness, better resolution, and more precision, the software infrastructure is lagging. We developed DTCS (Digital Twin for Chemical Science) v.01, a central platform that faithfully mimics advanced instrumentations in Scientific User Facilities, by solving a variety of technical challenges in data acquisition, analysis, and model-driven interpretation. Rooted in physics and accelerated by AI, we validated this concept by direct comparison with precise experimental X-ray Photoelectron Spectroscopy (XPS) observations using a ubiquitous metal-water interfacial scenario, i.e., Ag/H2O as our main narrative. The DTCS v.01 input mirrors how the bench chemists work, with the output directly linked to the end station computer, thereby providing a user-friendly, knowledge-driven, and accessible user experience with mechanistic insights standardized in a way that are ready to be published, versioned, and transferred flexibly.

Qian, Jin↗
DOE OSTI2019

Battery Thermal Characterization

A well-designed thermal management system is critical to the life and performance of electric-drive vehicles (EDVs) hybrids (HEVs), plug-in hybrids (PHEVs), and all-electric vehicles (EVs). Temperature and temperature uniformity both significantly affect the performance, lifespan, and safety of vehicle energy storage devices. NREL conducts thermal management research and development (R&D) to optimize battery performance and extend useful life. A recognized leader in battery thermal analysis and characterization, NREL evaluates electrical and thermal performance of battery cells, modules, and packs, full energy storage systems, and the interaction of these systems with other vehicle components. The lab's performance assessments factor in the design of the thermal management system, the thermal behavior of the cell, battery lifespan, and safety of the energy storage system, as well as full integration into a vehicle. The lithium-ion (Li-ion) batteries found in most of today's electric-drive vehicles are smaller and more lightweight than previous nickel-metal hydride (NiMH) technology, but they are also more sensitive to overheating, overcharging, and extreme spikes in temperature known as thermal runaway. High temperatures diminish battery life and increase battery replacement costs, while low temperatures decrease battery power and capacity, all of which impact vehicle range, performance, and affordability. NREL's breakthrough research is focused on reducing thermal barriers to achieve more uniform temperatures. NREL uses its R&D 100 Award-winning Isothermal Battery Calorimeters, the only instruments in the world capable of such precise thermal measurements, for much of this research. Calorimeter readings are paired with thermal imaging of batteries using infrared cameras to evaluate battery temperature distribution. In addition, NREL is working with industry to develop computer-aided engineering software tools to optimize thermal management of batteries. NREL's analysis for the U.S. Advanced Battery Consortium (USABC) show that optimized thermal management can increase battery power by more than 10%. Without proper thermal management, an EDV battery can last more than 10 years in a temperate climate, like in Minnesota, but only 7 years in a hot climate, such as in Arizona. In extreme instances, battery overheating can pose safety hazards, including fires. Performance: Maintaining desired temperature ranges is critical in optimizing EDV battery storage capacity, lifespan, and performance, and eventually impacts cost and consumer acceptance. NREL's breakthrough research focuses on achieving more uniform temperatures while a vehicle is in motion and actively generating heat, as well as when it is parked and exposed to ambient temperatures over prolonged periods of time. Safety: Most energy storage systems deliver maximum performance at higher temperatures. However, this extreme heat can overload systems and create dangerous conditions. NREL evaluation helps to more effectively regulate thermal characteristics, providing greater safeguards and stronger performance. NREL has invented a device that emulates internal short circuits in batteries to better understand how to prevent future failures and related hazards.

25 ENERGY STORAGE↗
DOE OSTI2015

Atmospheric-pressure plasma jet characterization and applications on melanoma cancer treatment (B/16-F10)

A new approach in medicine is the use of cold plasma for various applications such as sterilization blood coagulation and cancer cell treatment. In this paper, a pin-to-hole plasma jet for biological applications has been designed and manufactured and characterized. The characterization includes power consumption via Lissajous method, thermal behavior of atmospheric-pressure plasma jet by using Infra-red camera as a novel method and using Speicair software to determine vibrational and transitional temperatures, and optical emission spectroscopy to determine the generated species. Treatment of Melanoma cancer cells (B16/F10) was also implemented, and tetrazolium salt dye (MTT assay) and flow cytometry were used to evaluate viability. Effect of ultraviolet photons on cancerous cells was also observed using an MgF{sub 2} crystal with MTT assay. Finally, in-vivo studies on C57 type mice were also done in order to have a better understanding of the effects in real conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Software Defined Architectures for Data Analytics

Data analytics applications increasingly are complex workflows composed of phases with very different program behaviors (e.g., graph algorithms and machine learning, algorithms operating on sparse an dense data structures, etc). To reach the levels of efficiency required to process these workflows in real time, upcoming architectures will need to leverage even more workload specialization. If, at one end, we may find even more heterogenous processors composed by a myriad of specialized processing elements, at the other end we may see novel reconfigurable architectures, composed of sets of functional units and memories interconnected with (re)configurable on-chip networks, able to adapt dynamically to adapt the workload characteristics. Field Programmable Gate Arrays are more and more used for accelerating various workloads and, in particular, inferencing in machine learning, providing higher efficiency than other solutions. However, their fine grained nature still leads to issues for the design software and still makes dynamic reconfiguration impractical. Future, more coarse-grained architectures could offer the features to execute diverse workloads at high efficiency while providing better reconfiguration mechanisms for dynamic adaptability. Nevertheless, we argue that the challenges for reconfigurable computing remain in the software. In this position paper, we describe a possible toolchain for reconfigurable architectures targeted at data analytics.

FPGA, Dataflow, Reconfigurable Architectures, Soft↗
DOE OSTI2019

Behavior specification, finding main, and call graph visualizations

A process transforms compiled software into a semantic form. The process transforms the code into a semantic form. The process analyzes behavior functionality by processing precise programming behavior abstractions stored in a memory and classifies the code as malware based on the code behavior. Another method identifies the starting point of execution of a compiled program. The method calculates a complexity measure by calculating the number of potential execution paths of local functions; identifies the number of arguments passed to local functions; and identifies the starting point of execution of the compiled program. Another method provides interactive, dynamic visualization of a group of related functions wherein a user can explore the rendered graph and select a specific function and display functions that are color coded by their ancestral relation and their function call distance to the selected function.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Data-Conforming Data-Driven Control: Avoiding Premature Generalizations Beyond Data

Data-driven and adaptive control approaches face the problem of introducing sudden distributional shifts beyond the distribution of data encountered during learning. Therefore, they are prone to invalidating the very assumptions used in their own construction. This is due to the linearity of the underlying system, inherently assumed and formulated in most data-driven control approaches, which may falsely generalize the behavior of the system beyond the behavior experienced in the data. This article seeks to mitigate these problems by enforcing consistency of the newly designed closed-loop systems with data and slowing down any distributional shifts in the joint state-input space. This is achieved through incorporating affine regularization terms and linear matrix inequality constraints to data-driven approaches, resulting in convex semi-definite programs that can be efficiently solved by standard software packages. We discuss the optimality conditions of these programs and then conclude this article with a numerical example that further highlights the problem of premature generalization beyond data and shows the effectiveness of our proposed approaches in enhancing the safety of data-driven control methods.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2014

Anticipated Changes in Conducting Scientific Data-Analysis Research in the Big-Data Era

A Big-Data environment is one that is capable of orchestrating quick-turnaround analyses involving large volumes of data for numerous simultaneous users. Based on our experiences with a prototype Big-Data analysis environment, we anticipate some important changes in research behaviors and processes while conducting scientific data-analysis research in the near future as such Big-Data environments become the mainstream. The first anticipated change will be the reduced effort and difficulty in most parts of the data management process. A Big-Data analysis environment is likely to house most of the data required for a particular research discipline along with appropriate analysis capabilities. This will reduce the need for researchers to download local copies of data. In turn, this also reduces the need for compute and storage procurement by individual researchers or groups, as well as associated maintenance and management afterwards. It is almost certain that Big-Data environments will require a different "programming language" to fully exploit the latent potential. In addition, the process of extending the environment to provide new analysis capabilities will likely be more involved than, say, compiling a piece of new or revised code.We thus anticipate that researchers will require support from dedicated organizations associated with the environment that are composed of professional software engineers and data scientists. A major benefit will likely be that such extensions are of higherquality and broader applicability than ad hoc changes by physical scientists. Another anticipated significant change is improved collaboration among the researchers using the same environment. Since the environment is homogeneous within itself, many barriers to collaboration are minimized or eliminated. For example, data and analysis algorithms can be seamlessly shared, reused and re-purposed. In conclusion, we will be able to achieve a new level of scientific productivity in the Big-Data analysis environments.

Kuo, Kwo-Sen↗
NASA NTRS2017

A Compilation of MATLAB Scripts and Functions for MACGMC Analyses

The primary aim of the current effort is to provide scripts that automate many of the repetitive pre- and post-processing tasks associated with composite materials analyses using the Micromechanics Analysis Code with the Generalized Method of Cells. This document consists of a compilation of hundreds of scripts that were developed in MATLAB (The Mathworks, Inc., Natick, MA) programming language and consolidated into 16 MATLAB functions. (MACGMC). MACGMC is a composite material and laminate analysis software code developed at NASA Glenn Research Center. The software package has been built around the generalized method of cells (GMC) family of micromechanics theories. The computer code is developed with a user-friendly framework, along with a library of local inelastic, damage, and failure models. Further, application of simulated thermo-mechanical loading, generation of output results, and selection of architectures to represent the composite material have been automated to increase the user friendliness, as well as to make it more robust in terms of input preparation and code execution. Finally, classical lamination theory has been implemented within the software, wherein GMC is used to model the composite material response of each ply. Thus, the full range of GMC composite material capabilities is available for analysis of arbitrary laminate configurations as well. The pre-processing tasks include generation of a multitude of different repeating unit cells (RUCs) for CMCs and PMCs, visualization of RUCs from MACGMC input and output files and generation of the RUC section of a MACGMC input file. The post-processing tasks include visualization of the predicted composite response, such as local stress and strain contours, damage initiation and progression, stress-strain behavior, and fatigue response. In addition to the above, several miscellaneous scripts have been developed that can be used to perform repeated Monte-Carlo simulations to enable probabilistic simulations with minimal manual intervention. This document is formatted to provide MATLAB source files and descriptions of how to utilize them. It is assumed that the user has a basic understanding of how MATLAB scripts work and some MATLAB programming experience.

Materials Engineering↗
DOE OSTI2018

Effect of Initial FeO Content and CaO:SiO{sub 2} Ratio on the Reduction Smelting Kinetics of the CaO-SiO{sub 2}-MgO{sub satd.}-FeO Slag System

The effect of the initial FeO content and CaO:SiO{sub 2} ratio (CaO mass pct/SiO{sub 2} mass pct) on the reduction smelting of FeO with carbon flake addition is investigated in the CaO-MgO{sub satd.}-SiO{sub 2}-FeO slag system at 1823 K (1550 °C). Carbon rapidly reacted with FeO in the molten slag, causing both foaming and compositional changes in the slag. As FeO is reduced, the MgO saturation is modified, and solid precipitants, including MgO and other complex oxides, were observed, which significantly affected the slag properties, including the viscosity and foaming behavior. The solid-phase fraction and viscosity were estimated from changes in the measured FeO content over time using the thermochemical software FactSage. The iron recovery, which is distinguished from the amount of reduced Fe droplets, showed opposite behavior to the measured maximum foaming height and modified foaming index. According to the FeO mass transfer coefficient considering slag foaming at various initial FeO contents and CaO:SiO{sub 2} ratios, the reduction rate was optimal at higher initial FeO contents and a CaO:SiO{sub 2} ratio of 2.0, which did not correspond to the optimal iron recovery at an initial FeO content of 44 mass pct and above and a CaO:SiO{sub 2} ratio of 1.2. The results showed that slag foaming may increase the reduction kinetics, but the slag composition needs to be optimized for greater iron recovery.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Benchmark Generation and Simulation at Extreme Scale

The path to extreme scale high-performance computing (HPC) poses several challenges related to power, performance, resilience, productivity, programmability, data movement, and data management. Investigating the performance of parallel applications at scale on future architectures and the performance impact of different architectural choices is an important component of HPC hardware/software co-design. Simulations using models of future HPC systems and communication traces from applications running on existing HPC systems can offer an insight into the performance of future architectures. This work targets technology developed for scalable application tracing of communication events. It focuses on extreme-scale simulation of HPC applications and their communication behavior via lightweight parallel discrete event simulation for performance estimation and evaluation. Instead of simply replaying a trace within a simulator, this work promotes the generation of a benchmark from traces. This benchmark is subsequently exposed to simulation using models to reflect the performance characteristics of future-generation HPC systems. This technique provides a number of benefits, such as eliminating the data intensive trace replay and enabling simulations at different scales. The presented work features novel software co-design aspects, combining the ScalaTrace tool to generate scalable trace files, the ScalaBenchGen tool to generate the benchmark, and the xSim tool to assess the benchmark characteristics within a simulator.

Lagadapati, Mahesh↗
DOE OSTI2019

An Integrated Numerical Method to Predict the Strain Sensing Behavior of Flexible Conductive Fibers under Electric–Thermal Conditions

Flexible conductive textiles have attracted an increasing amount of attention due to being flexible and stretchable with multi-functions, in addition to the strain sensing function. In this paper, an integrated numerical method is established to predict the strain sensing behavior of PPy-coated flexible conductive fibers. Based on our previous investigation on the strain sensing behavior of the PPy-coated elastic conductive fibers, it has been known that the main strain sensing mechanisms of the conductive fibers are micro-cracks opening under stretching and closing under unloading. Therefore, in the numerical approach, extended finite element method is used to simulate the multi-crack initiation and propagation. Then, by establishing a representative element model including a single crack, the resistance variation with the increase in crack width/depth can be obtained using the electric–thermal package in ABAQUS software. Combining the relationship between crack width and strain, the relationship between fractional increment in resistance (ΔR/R{sub 0}) and the strain can be obtained. The prediction of the fractional increment in resistance (ΔR/R{sub 0}) versus strain is consistent with the experimental results of PPy-coated Lycra fibers. The new integrated method is further verified by experimental results of PPy-coated XLA fibers, showing that the integrated numerical approach can capture the complicated strain sensing mechanisms of PPy-coated elastic fibers and predict the strain sensing behavior of flexible conductive fibers. This approach will make it possible to shorten the period, and reduce the cost in R&D of the PPy-coated electrically conductive fibers, comparing with the corresponding experimental work.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Comparison of subchannel and averaged channel thermal-hydraulic descriptions on coupled pin-by-pin neutronic calculations

Best-estimate transient analysis of Light Water Reactors (LWRs) requires high accuracy code systems solving neutronics (NK), thermal-hydraulics (TH) and thermal-mechanics to model the core behavior. For long time, nodal diffusion methods for solution of the transport equation using assembly size computational meshes have been applied neglecting the details within the fuel assembly, or using 'pin power reconstruction' techniques to predict those details. Moreover, the multiphysics analysis has been commonly performed coupling those neutronic diffusion methods to thermal-hydraulic system codes using a nodalization of several fuel assemblies per computational node without taking into account the effects that this type of averaging can have on the results obtained. This paper presents the work accomplished with the coupled system COBAYA4/CTF for a preliminary analysis of the effect of the different accuracy levels in the NK and TH domain. COBAYA4 is a neutronic code able to solve the diffusion equation at two resolution levels, nodal and pin by pin (PbP). CTF is a thermo-hydraulic code able to solve the coolant conservation equations also at two levels, channel (fuel assembly averaged solution) and sub-channel (real cooling channel) level. The coupled system COBAYA4/CTF is used through SALOME platform, i.e. SALOME is the software able to map conveniently the NK and TH meshes. This coupling allows combining different resolution levels for NK and TH problems. Therefore, the study of the effect of using different resolutions for the NK and TH nodalizations has been performed applying the system to a fuel assembly with selected boundary conditions. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Asynchronous Runtimes in Action: An Introspective Framework for a Next Gen Runtime

One of the most critical challenges that new high performance systems face is the lack of system software support for these large scale systems. Investment on system stack components is essential in the development, debugging and optimization of the new emerging programming models. These emerging models have the promise to better utilize the vast hardware resources available in current and future systems. To aid in the development of applications and new system stacks, runtimes, as instances of their respective execution models, need to produce facilities to introspect their inner workings and allow an indepth attribution of performance bottlenecks and computational patterns. In other words, the runtime systems need to reduce their opacity to observers so that users of a novel program execution model can adapt their designs to fit the intended model usage, regardless of the layer that they are working on. This design/development loop (akin to co-design) enables synergistic opportunities across the entire computational stack. This paper presents the design and implementation of a simple “gray” box performance attribution harness running inside a fine grain runtime system: the Open Community Runtime (OCR). We showcase what such a framework can indicate regarding the runtime behavior while running at scale. To this end, we have designed a set of synthetic scenarios aimed to test the runtime at their best and worst cases. We present an analysis of the most important runtime features, properties and idiosyncrasies that will affect the development of new runtime features, algorithmic selection, and application development.

high performance distributed runtimes↗
DOE OSTI2018

Air-Coupled Nondestructive Evaluation Dissected

This work develops a two-dimensional theoretical model to simulate the behavior of a fully non-contact air-coupled nondestructive evaluation system for a thin isotropic plate. The model is divided into transmission, guided wave propagation and reception phase. The validation of the complete model was carried out by modeling the same system by means of finite element method using a Multiphysics software. In addition, the dependency of the generated Lamb waves on different transmitter’s parameters and incidence angle is thoroughly investigated. The results of the acoustic pressure excited by the transducer, the out-of-plane velocity amplitudes for the generated first antisymmetric Lamb wave mode, and the radiated pressure from the plate caused by the leaky Lamb wavefield were all compared between the two models and a reasonable degree of similarity was found.

42 ENGINEERING↗
DOE OSTI2022

Hierarchical Model-Free Transactive Control of Building Loads to Support Grid Services

Residential buildings consume 4.4 quads of electricity annually, approximately 37% of the total electricity consumption in the United States. This represents a vast resource that can be used for demand management and other ancillary services. This project aims to develop a robust, scalable hierarchical transactional control mechanism incorporating elements of model-free control (MFC) and game theory to harness buildings to provide ancillary services to the grid. This approach is being taken to address the challenges of incorporating traditional transactional control schemes into existing buildings. The challenges include small individual building sizes requiring aggregation of many buildings, unpredictable energy usage that makes model identification difficult, and satisfying the sensitive occupant comfort constraints. In the proposed approach, by separating the control mechanism into two layers above and below the load aggregator, MFC can be used below the aggregator to modulate flexible building loads in response to pricing signals with guaranteed performance. This allows the burden of identifying an accurate model of the system to be shifted to the above-aggregator layer, where fluctuations in individual building usage have less impact on predicted building system behavior. Game theory concepts can then be used to determine pricing curves and control signals among regional aggregators. Managing this control in a game-theoretic approach will allow us to build in financial incentives that increase customer engagement. Additionally, the usage of MFC necessitates less burdensome computational and communication requirements, thus, it is easily deployable on small, embedded devices. In a broader sense, developing a strategy capable of effectively incorporating residential and small commercial buildings will allow greater throughput of existing and emerging grid services in addition to future transactive energy grid management methods. Using MFC within a hierarchical control architecture will allow the shifting of existing forecasting challenges to an aggregate level, where dynamics are slower and more predictable. This will enable a smooth interface between the grid services requests of utilities and the reliable control required by participating buildings. MFC, which supports distributed control architecture, permits a scalable solution that can be deployed to neighborhood-size systems as well as individual buildings. This project focuses on three objectives: (1) developing the mathematical framework, algorithm toolkit, and software toolset of the two-layer transactive control testbed; (2) developing a scalable solution for application over many residential and small-size commercial buildings with sparse distributed communication; and (3) field testing and implementation on hardware of the control strategies developed in the previous two objectives. The research and development activities are focused and designed to be impactful within the relevant 2025 targets timeframe. An open-source control framework for exploiting variability and dispatchability of building loads will be delivered as the outcome of the project. This capability enables greater participation of loads in electricity markets and ancillary services that are both useful for the utility and financially beneficial for building owners.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Uncertainty Quantification Analysis of Both Experimental and CFD Simulation Data of a Bench-scale Fluidized Bed Gasifier

Adequate assessment of the uncertainties in modeling and simulation is becoming an integral part of the simulation based engineering design. The goal of this study is to demonstrate the application of non-intrusive Bayesian uncertainty quantification (UQ) methodology in multiphase (gas-solid) flows with experimental and simulation data, as part of our research efforts to determine the most suited approach for UQ of a bench scale fluidized bed gasifier. UQ analysis was first performed on the available experimental data. Global sensitivity analysis performed as part of the UQ analysis shows that among the three operating factors, steam to oxygen ratio has the most influence on syngas composition in the bench-scale gasifier experiments. An analysis for forward propagation of uncertainties was performed and results show that an increase in steam to oxygen ratio leads to an increase in H2 mole fraction and a decrease in CO mole fraction. These findings are in agreement with the ANOVA analysis performed in the reference experimental study. Another contribution in addition to the UQ analysis is the optimization-based approach to guide to identify next best set of additional experimental samples, should the possibility arise for additional experiments. Hence, the surrogate models constructed as part of the UQ analysis is employed to improve the information gain and make incremental recommendation, should the possibility to add more experiments arise. In the second step, series of simulations were carried out with the open-source computational fluid dynamics software MFiX to reproduce the experimental conditions, where three operating factors, i.e., coal flow rate, coal particle diameter, and steam-to-oxygen ratio, were systematically varied to understand their effect on the syngas composition. Bayesian UQ analysis was performed on the numerical results. As part of Bayesian UQ analysis, a global sensitivity analysis was performed based on the simulation results, which shows that the predicted syngas composition is strongly affected not only by the steam-to-oxygen ratio (which was observed in experiments as well) but also by variation in the coal flow rate and particle diameter (which was not observed in experiments). The carbon monoxide mole fraction is underpredicted at lower steam-to-oxygen ratios and overpredicted at higher steam-to-oxygen ratios. The opposite trend is observed for the carbon dioxide mole fraction. These discrepancies are attributed to either excessive segregation of the phases that leads to the fuel-rich or -lean regions or alternatively the selection of reaction models, where different reaction models and kinetics can lead to different syngas compositions throughout the gasifier. To improve quality of numerical models used, the effect that uncertainties in reaction models for gasification, char oxidation, carbon monoxide oxidation, and water gas shift will have on the syngas composition at different grid resolution, along with bed temperature were investigated. The global sensitivity analysis showed that among various reaction models employed for water gas shift, gasification, char oxidation, the choice of reaction model for water gas shift has the greatest influence on syngas composition, with gasification reaction model being second. Syngas composition also shows a small sensitivity to temperature of the bed. The hydrodynamic behavior of the bed did not change beyond grid spacing of 18 times the particle diameter. However, the syngas concentration continued to be affected by the grid resolution as low as 9 times the particle diameter. This is due to a better resolution of the phasic interface between the gases and solid that leads to stronger heterogeneous reactions. This report is a compilation of three manuscripts published in peer-reviewed journals for the series of studies mentioned above.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Transition of SRT Source Term Software to Modern Version Control System to Enable More Efficient Verification, Validation, and User Engagement

The Simplified Radionuclide Transport (SRT) code assists in reactor design and licensing by providing a detailed analysis of radionuclide transport under specific transient scenarios. This analysis is essential for ensuring that reactors meet safety and regulatory standards before they are authorized to operate. Mechanistic source term analysis, which involves a realistic evaluation of radionuclide behavior from the source to the environment, is a key component of this process. It provides a comprehensive understanding of potential release scenarios, supporting the development of robust safety measures.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗