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DOE OSTI2015

Framework for 3D histologic reconstruction and fusion with in vivo MRI: Preliminary results of characterizing pulmonary inflammation in a mouse model

Purpose: Pulmonary inflammation is associated with a variety of diseases. Assessing pulmonary inflammation on in vivo imaging may facilitate the early detection and treatment of lung diseases. Although routinely used in thoracic imaging, computed tomography has thus far not been compellingly shown to characterize inflammation in vivo. Alternatively, magnetic resonance imaging (MRI) is a nonionizing radiation technique to better visualize and characterize pulmonary tissue. Prior to routine adoption of MRI for early characterization of inflammation in humans, a rigorous and quantitative characterization of the utility of MRI to identify inflammation is required. Such characterization may be achieved by considering ex vivo histology as the ground truth, since it enables the definitive spatial assessment of inflammation. In this study, the authors introduce a novel framework to integrate 2D histology, ex vivo and in vivo imaging to enable the mapping of the extent of disease from ex vivo histology onto in vivo imaging, with the goal of facilitating computerized feature analysis and interrogation of disease appearance on in vivo imaging. The authors’ framework was evaluated in a preclinical preliminary study aimed to identify computer extracted features on in vivo MRI associated with chronic pulmonary inflammation. Methods: The authors’ image analytics framework first involves reconstructing the histologic volume in 3D from individual histology slices. Second, the authors map the disease ground truth onto in vivo MRI via coregistration with 3D histology using the ex vivo lung MRI as a conduit. Finally, computerized feature analysis of the disease extent is performed to identify candidate in vivo imaging signatures of disease presence and extent. Results: The authors evaluated the framework by assessing the quality of the 3D histology reconstruction and the histology—MRI fusion, in the context of an initial use case involving characterization of chronic inflammation in a mouse model. The authors’ evaluation considered three mice, two with an inflammation phenotype and one control. The authors’ iterative 3D histology reconstruction yielded a 70.1% ± 2.7% overlap with the ex vivo MRI volume. Across a total of 17 anatomic landmarks manually delineated at the division of airways, the target registration error between the ex vivo MRI and 3D histology reconstruction was 0.85 ± 0.44 mm, suggesting that a good alignment of the ex vivo 3D histology and ex vivo MRI had been achieved. The 3D histology-in vivo MRI coregistered volumes resulted in an overlap of 73.7% ± 0.9%. Preliminary computerized feature analysis was performed on an additional four control mice, for a total of seven mice considered in this study. Gabor texture filters appeared to best capture differences between the inflamed and noninflamed regions on MRI. Conclusions: The authors’ 3D histology reconstruction and multimodal registration framework were successfully employed to reconstruct the histology volume of the lung and fuse it with in vivo MRI to create a ground truth map for inflammation on in vivo MRI. The analytic platform presented here lays the framework for a rigorous validation of the identified imaging features for chronic lung inflammation on MRI in a large prospective cohort.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-J-14: Kilovoltage Cone-Beam CT Dose Estimation of Varian On-Board Imager Using GMctdospp Monte Carlo Framework

Purpose: Although cone-beam CT (CBCT) imaging became popular in radiation oncology, its imaging dose estimation is still challenging. The goal of this study is to assess the kilovoltage CBCT doses using GMctdospp - an EGSnrc based Monte Carlo (MC) framework. Methods: Two Varian OBI x-ray tube models were implemented in the GMctpdospp framework of EGSnrc MC System. The x-ray spectrum of 125 kVp CBCT beam was acquired from an EGSnrc/BEAMnrc simulation and validated with IPEM report 78. Then, the spectrum was utilized as an input spectrum in GMctdospp dose calculations. Both full and half bowtie pre-filters of the OBI system were created by using egs-prism module. The x-ray tube MC models were verified by comparing calculated dosimetric profiles (lateral and depth) to ion chamber measurements for a static x-ray beam irradiation to a cuboid water phantom. An abdominal CBCT imaging doses was simulated in GMctdospp framework using a 5-year-old anthropomorphic phantom. The organ doses and effective dose (ED) from the framework were assessed and compared to the MOSFET measurements and convolution/superposition dose calculations. Results: The lateral and depth dose profiles in the water cuboid phantom were well matched within 6% except a few areas - left shoulder of the half bowtie lateral profile and surface of water phantom. The organ doses and ED from the MC framework were found to be closer to MOSFET measurements and CS calculations within 2 cGy and 5 mSv respectively. Conclusion: This study implemented and validated the Varian OBI x-ray tube models in the GMctdospp MC framework using a cuboid water phantom and CBCT imaging doses were also evaluated in a 5-year-old anthropomorphic phantom. In future study, various CBCT imaging protocols will be implemented and validated and consequently patient CT images will be used to estimate the CBCT imaging doses in patients.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

PARTONS: PARtonic Tomography Of Nucleon Software

We describe the architecture and functionalities of a C++ software framework, coined PARTONS, dedicated to the phenomenology of Generalized Parton Distributions. These distributions describe the three-dimensional structure of hadrons in terms of quarks and gluons, and can be accessed in deeply exclusive lepto- or photo-production of mesons or photons. PARTONS provides a necessary bridge between models of Generalized Parton Distributions and experimental data collected in various exclusive production channels. We outline the specification of the PARTONS framework in terms of practical needs, physical content and numerical capacity. This framework will be useful for physicists - the-orists or experimentalists - not only to develop new models, but also to interpret existing measurements and even design new experiments

Berthou, B.↗

DOE OSTI2017

A Data Driven Pre-cooling Framework for Energy Cost Optimization in Commercial Buildings

Commercial buildings consume significant amount of energy. Facility managers are increasingly grappling with the problem of reducing their buildings’ peak power, overall energy consumption and energy bills. In this paper, we first develop an optimization framework – based on a gray box model for zone thermal dynamics – to determine a pre-cooling strategy that simultaneously shifts the peak power to low energy tariff regimes, and reduces both the peak power and overall energy consumption by exploiting the flexibility in a building’s thermal comfort range. We then evaluate the efficacy of the pre-cooling optimization framework by applying it to building management system data, spanning several days, obtained from a large commercial building located in a tropical region of the world. The results from simulations show that optimal pre-cooling reduces peak power by over 50%, energy consumption by up to 30% and energy bills by up to 37%. Next, to enable ease of use of our framework, we also propose a shortest path based heuristic algorithmfor solving the optimization problemand show that it has comparable erformance with the optimal solution. Finally, we describe an application of the proposed optimization framework for developing countries to reduce the dependency on expensive fossil fuels, which are often used as a source for energy backup.We conclude by highlighting our real world deployment of the optimal pre-cooling framework via a software service on the cloud platform of a major provider. Our pre-cooling methodology, based on the gray box optimization framework, incurs no capital expense and relies on data readily available from a building management system, thus enabling facility managers to take informed decisions for improving the energy and cost footprints of their buildings

Vishwanath, Arun↗

DOE OSTI2016

Advanced Computational Framework for Environmental Management ZEM, Version 1.x

Typically environmental management problems require analysis of large and complex data sets originating from concurrent data streams with different data collection frequencies and pedigree. These big data sets require on-the-fly integration into a series of models with different complexity for various types of model analyses where the data are applied as soft and hard model constraints. This is needed to provide fast iterative model analyses based on the latest available data to guide decision-making. Furthermore, the data and model are associated with uncertainties. The uncertainties are probabilistic (e.g. measurement errors) and non-probabilistic (unknowns, e.g. alternative conceptual models characterizing site conditions). To address all of these issues, we have developed an integrated framework for real-time data and model analyses for environmental decision-making called ZEM. The framework allows for seamless and on-the-fly integration of data and modeling results for robust and scientifically-defensible decision-making applying advanced decision analyses tools such as Bayesian- Information-Gap Decision Theory (BIG-DT). The framework also includes advanced methods for optimization that are capable of dealing with a large number of unknown model parameters, and surrogate (reduced order) modeling capabilities based on support vector regression techniques. The framework is coded in Julia, a state-of-the-art high-performance programing language (http://julialang.org). The ZEM framework is open-source and can be applied to any environmental management site. The framework will be open-source and released under GPL V3 license.

Vesselinov, VelimirV.↗

DOE OSTI2011

A general framework for modeling growth and division of mammalian cells

Background: Modeling the cell-division cycle has been practiced for many years. As time has progressed, this work has gone from understanding the basic principles to addressing distinct biological problems, e.g., the nature of the restriction point, how checkpoints operate, the nonlinear dynamics of the cell cycle, the effect of localization, etc. Most models consist of coupled ordinary differential equations developed by the researchers, restricted to deal with the interactions of a limited number of molecules. In the future, cell-cycle modeling–and indeed all modeling of complex biologic processes–will increase in scope and detail. Results: A framework for modeling complex cell-biologic processes is proposed here. The framework is based on two constructs: one describing the entire lifecycle of a molecule and the second describing the basic cellular machinery. Use of these constructs allows complex models to be built in a straightforward manner that fosters rigor and completeness. To demonstrate the framework, an example model of the mammalian cell cycle is presented that consists of several hundred differential equations of simple mass action kinetics. The model calculates energy usage, amino acid and nucleotide usage, membrane transport, RNA synthesis and destruction, and protein synthesis and destruction for 33 proteins to give an in-depth look at the cell cycle. Conclusions: The framework presented here addresses how to develop increasingly descriptive models of complex cell-biologic processes. The example model of cellular growth and division constructed with the framework demonstrates that large structured models can be created with the framework, and these models can generate non-trivial descriptions of cellular processes. Predictions from the example model include those at both the molecular level–e.g., Wee1 spontaneously reactivates–and at the system level–e.g., pathways for timing-critical processes must shut down redundant pathways. A future effort is to automatically estimate parameter values that are insensitive to changes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2011

A Framework for Comparative Assessments of Energy Efficiency Policy Measures

When policy makers propose new policies, there is a need to assess the costs and benefits of the proposed policy measures, to compare them to existing and alternative policies, and to rank them according to their effectiveness. In the case of equipment energy efficiency regulations, comparing the effects of a range of alternative policy measures requires evaluating their effects on consumers’ budgets, on national energy consumption and economics, and on the environment. Such an approach should be able to represent in a single framework the particularities of each policy measure and provide comparable results. This report presents an integrated methodological framework to assess prospectively the energy, economic, and environmental impacts of energy efficiency policy measures. The framework builds on the premise that the comparative assessment of energy efficiency policy measures should (a) rely on a common set of primary data and parameters, (b) follow a single functional approach to estimate the energy, economic, and emissions savings resulting from each assessed measure, and (c) present results through a set of comparable indicators. This framework elaborates on models that the U.S. Department of Energy (DOE) has used in support of its rulemakings on mandatory energy efficiency standards. In addition to a rigorous analysis of the impacts of mandatory standards, DOE compares the projected results of alternative policy measures to those projected to be achieved by the standards. The framework extends such an approach to provide a broad, generic methodology, with no geographic or sectoral limitations, that is useful for evaluating any type of equipment energy efficiency market intervention. The report concludes with a demonstration of how to use the framework to compare the impacts estimated for twelve policy measures focusing on increasing the energy efficiency of gas furnaces in the United States.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2013

Development of Parallel Computing Framework to Enhance Radiation Transport Code Capabilities for Rare Isotope Beam Facility Design

A parallel computing framework has been developed to use with general-purpose radiation transport codes. The framework was implemented as a C++ module that uses MPI for message passing. It is intended to be used with older radiation transport codes implemented in Fortran77, Fortran 90 or C. The module is significantly independent of radiation transport codes it can be used with, and is connected to the codes by means of a number of interface functions. The framework was developed and tested in conjunction with the MARS15 code. It is possible to use it with other codes such as PHITS, FLUKA and MCNP after certain adjustments. Besides the parallel computing functionality, the framework offers a checkpoint facility that allows restarting calculations with a saved checkpoint file. The checkpoint facility can be used in single process calculations as well as in the parallel regime. The framework corrects some of the known problems with the scheduling and load balancing found in the original implementations of the parallel computing functionality in MARS15 and PHITS. The framework can be used efficiently on homogeneous systems and networks of workstations, where the interference from the other users is possible.

43 PARTICLE ACCELERATORS↗

DOE OSTI2015

Status on the Development of a Modeling and Simulation Framework for the Economic Assessment of Nuclear Hybrid Energy Systems

An effort to design and build a modeling and simulation framework to assess the economic viability of Nuclear Hybrid Energy Systems (NHES) was undertaken in fiscal year 2015 (FY15). The purpose of this report is to document the various tasks associated with the development of such a framework and to provide a status on its progress. Several tasks have been accomplished. First, starting from a simulation strategy, a rigorous mathematical formulation has been achieved in which the economic optimization of a Nuclear Hybrid Energy System is presented as a constrained robust (under uncertainty) optimization problem. Some possible algorithms for the solution of the optimization problem are presented. A variation of the Simultaneous Perturbation Stochastic Approximation algorithm has been implemented in RAVEN and preliminary tests have been performed. The development of the software infrastructure to support the simulation of the whole NHES has also moved forward. The coupling between RAVEN and an implementation of the Modelica language (OpenModelica) has been implemented, migrated under several operating systems and tested using an adapted model of a desalination plant. In particular, this exercise was focused on testing the coupling of the different code systems; testing parallel, computationally expensive simulations on the INL cluster; and providing a proof of concept for the possibility of using surrogate models to represent the different NHES subsystems. Another important step was the porting of the RAVEN code under the Windows™ operating system. This accomplishment makes RAVEN compatible with the development environment that is being used for dynamic simulation of NHES components. A very simplified model of a NHES on the electric market has been built in RAVEN to confirm expectations on the analysis capability of RAVEN to provide insight into system economics and to test the capability of RAVEN to identify limit surfaces even for stochastic constraints. This capability will be needed in the future to enforce the stochastic constraints on the electric demand coverage from the NHES. The development team gained experience with many of the tools that are currently envisioned for use in the economic analysis of NHES and completed several important steps. Given the complexity of the project, preference has been given to a structural approach in which several independent efforts have been used to build the cornerstone of the simulation framework. While this is good approach in establishing such a complex framework, it may delay reaching more complete results on the performance of analyzed system configurations. The integration of the previously reported exergy analysis approach was initially proposed as part of this milestone. However, in reality, the exergy-based apportioning of cost will take place only in a second stage of the implementation since it will be used to properly allocate cost among the different NHES subsystems. Therefore, exergy does not appear at the level of the main drivers in the analysis framework; the latter development of the base framework is the focus of this report.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2015

Preliminary Framework for Human-Automation Collaboration

The Department of Energy’s Advanced Reactor Technologies Program sponsors research, development and deployment activities through its Next Generation Nuclear Plant, Advanced Reactor Concepts, and Advanced Small Modular Reactor (aSMR) Programs to promote safety, technical, economical, and environmental advancements of innovative Generation IV nuclear energy technologies. The Human Automation Collaboration (HAC) Research Project is located under the aSMR Program, which identifies developing advanced instrumentation and controls and human-machine interfaces as one of four key research areas. It is expected that the new nuclear power plant designs will employ technology significantly more advanced than the analog systems in the existing reactor fleet as well as utilizing automation to a greater extent. Moving towards more advanced technology and more automation does not necessary imply more efficient and safer operation of the plant. Instead, a number of concerns about how these technologies will affect human performance and the overall safety of the plant need to be addressed. More specifically, it is important to investigate how the operator and the automation work as a team to ensure effective and safe plant operation, also known as the human-automation collaboration (HAC). The focus of the HAC research is to understand how various characteristics of automation (such as its reliability, processes, and modes) effect an operator’s use and awareness of plant conditions. In other words, the research team investigates how to best design the collaboration between the operators and the automated systems in a manner that has the greatest positive impact on overall plant performance and reliability. This report addresses the Department of Energy milestone M4AT-15IN2302054, Complete Preliminary Framework for Human-Automation Collaboration, by discussing the two phased development of a preliminary HAC framework. The framework developed in the first phase was used as the basis for selecting topics to be investigated in more detail. The results and insights gained from the in-depth studies conducted during the second phase were used to revise the framework. This report describes the basis for the framework developed in phase 1, the changes made to the framework in phase 2, and the basis for the changes. Additional research needs are identified and presented in the last section of the report.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2015

Draft Infrastructure Framework for a Generic Repository Licensing Organization

This document is milestone M4FT-15SN0826011: Draft Summary of generic repository licensing infrastructure, the result of efforts under work Package FT-15SN082601 DOE Managed HLW and SNF Research: Establish a generic organization framework for records management, IT infrastructure, quality assurance, and systems engineering - Sandia National Laboratories (SNL). The objective of this effort was to develop this draft document identifying and summarizing the principle elements of a generic repository licensing infrastructure organizational framework. The FY16 follow on work package is FT-16SN050302- Establish organizational framework to meet regulator expectations - SNL, having the consistent objective to 'Develop a draft document identifying and summarizing the principle elements of a generic repository licensing infrastructure and organizational framework.' This document describes the framework for a generic hypothetical organization with the responsibility to site, characterize, design, license construct and operate a repository for the disposal of high-level radioactive waste (HLW) and spent nuclear fuel ( F) managed by the Department of Energy. organizational framework suggested is intended to be agnostic of the facility location and disposal medium. It is assumed that the facility is to be licensed under U.S. Nuclear Regulatory Commission (NRC) regulation with DOE as the applicant (licensee). Detailed organizational elements reflect a workforce composition and practices that facilitate compliance with NRC expectations and NQA-1 quality standards.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

System Modeling Frameworks for Wind Turbines and Plants: Review and Requirements Specifications

System modeling frameworks for wind turbines and plants are used by research groups and industry to design wind energy systems that take into account key trade-offs across performance, cost, and reliability at both the turbine and plant level. The frameworks are exercised using a variety of multi-disciplinary design, analysis and optimization (MDAO) methods. To improve inter-operability and foster collaboration, this report proposes a classification system for the frameworks along dimensions of model fidelity and scope. The classification system is first motivated with reviews the state-of-the-art in the development of software frameworks for integrated wind turbine and plant simulation. Within each major wind turbine and power plant subsystem, a matrix is developed for the disciplines used and the fidelity levels with which each discipline can be modeled. The existing frameworks are then classified according to the matrix. Next, an ontology is proposed that will allow for standardizing how data is transferred between the most common discipline-fidelity combinations used in the frameworks. A common representation of data creates the ability to 1) share system descriptions and analysis results, supporting more transparent benchmarks and comparison, and 2) integrate models together into workflows within and across organizations for improving the efficiency and performance of wind turbine and power plant design processes. Ultimately, this integration leads to better overall wind energy system designs with high performance and low costs.

17 WIND ENERGY↗

DOE OSTI2021

SIS-AOP Cueing/Segmenting Algorithm (FOA_SIS-AOP) Using the Sandia FOA 4.0 Framework

For machine vision, one of the most important operations is fast and effective object cueing or segmentation. Sandia National Labs has a long history of development and implementation of very fast and effective cueing/segmentation algorithms. This report covers the history, motivation and implementation of evolving frameworks (Sandia FOA Frameworks) upon which this long legacy of successful algorithms are built. The report describes the innovative microprocessor implementation, enabling extremely fast morphological processing, combined with a novel adaptive quantization front - end and a feature - based backend that resulted in Sandia developing fast and effective cueing in a wide variety of applications, from defect detection to SAR ATR. The report covers evolution from Sandia FOA 1.0 Framework (1995) to current Sandia FOA 4.0 Framework (2021). Requirements for the cueing algorithm for SIS - AOP (FOA_SIS - AOP) that drove the Sandia FOA 4.0 Framework development are discussed, along with information on how to use the Sandia FOA Frameworks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Hydro-Code Implementation and Testing of a Kinetic Phase Transition Framework

In this report we describe the Kinetic Phase Transition (KPT) framework that has been worked out over the last 10 years (from around 2014) and the implementation of it into three different codes, the one-dimensional hydro- LASLO and the three-dimensional magneto-hydro- ALEGRA, Sandia codes, via subroutines in the LAMBDA Equations of State and constitutive models package, and Flag, an arbitrary Lagrangian-Eulerian multiphysics code developed within the Lagrangian Applications project (LAP) at LANL. We discuss the introduction of phase mass (and/or volume) fractions that are needed in a code for it to be ‘phase aware’, that is, not only the thermodynamic state is known in each point but also the mixture of the materials’ phases in that point. Further we point to the need of a full Equations of State for each phase in a material to achieve phase awareness and we review the equilibrium phase model, where a phase mixture is at its lowest Gibbs free energy state, to make this point clear. Contrasting the kinetic phase transition to this equilibrium model seamlessly introduce us to the KPT framework that is subsequently thoroughly discussed. While the determination of the total state and the states and mass fractions of phases in each point is a problem that can borrow many of its numerical details from Eulerian codes and mixture of materials (not phases), the update of mass fractions with time in a KPT framework needs a new set of considerations. General for any update model is that we need to prevent mass fractions from becoming unphysical (negative or their sum to be larger than one). We have solved this problem by implementing a subdivision of the hydro time step that prevents the phase from being fully present to not present at all in one subdivided time step by limiting the size of the subdivided time step. This scheme also corrects numerical problems from abrupt changes in parameter values, the so called Gibbs phenomena, that gives rise to slushing between phases in the KPT framework. Interspersed throughout the report are discussions on different thermodynamics considerations. EOS validity windows, limitations on the EOS phase space, are needed for the KPT framework and are discussed separately and exemplified. The KPT framework described in this report has been verified by code comparison, but validation is still an active area of research. There is room for improvement in the update model, both in the model for determination of rates and in how to prevent the mass fractions from becoming unphysical. In addition, the parameters in the KPT update model and the placement of the phase boundary in the EOS phase space, and interactions with other constitutive models, are closely related and interfering with each other. One possible way forward is to simultaneously develop KPT parameters, EOS, and constitutive models for each material.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

LOCOMOTIVES - Comprehensive Impact and Cost Assessment Framework of Carbon Lowering Approaches for the US Rail Freight System

The goal of this project is to develop a tool to aid railroads and other stakeholders assess and approach the decarbonization of freight rail operations by identifying new, viable low-carbon energy storage and conversion systems for future locomotive systems and how they should be deployed on the existing US freight rail network. In the first quarter, the project focused on collecting data, establishing a simulation workflow, and engaging industry through the creation of the Industry Advisory Board (IAB). In the second quarter, the project focused on selecting fuel pathways and powertrain technologies, setting performance targets, conducting a techno-economic analyses, and developing the simulation framework that would serve as the backbone of the future toolhead. The third quarter involved developing an industry-oriented interactive dashboard powered by a five-step sequential framework, as well as holding industry advisory board meetings as per the initial technology-to-market plan. In the remaining project quarters, the NUFRIEND dashboard were fine-tuned with the help of IAB member feedback and in-depth scenario analyses were conducted to support the techno-economic analysis of energy sources. Additionally, dashboard documentation, project insights, and open-source code on GitHub were prepared and released. Throughout the project, the team completed testing and analysis of all model components, integrated all initial test scenarios, and conducted stakeholder engagement. Lower-carbon drop-in fuels can be deployed as admixtures and are considered uniform across the network at a desired penetration rate, while hydrogen and battery-electric technology deployment poses a more complex problem as they require significant investments to be made in the siting of refueling/charging facilities and the replacement of locomotive fleets. Thus, strategies for locating and sizing refueling/charging facilities on a railroad’s network to meet their energy demands were developed to inform deployment decisions. To address this challenge, the Northwestern University Freight Rail Infrastructure & Energy Network Decarbonization (NUFRIEND) framework presents a five-step sequential framework to select O-D paths, locate facilities, reroute flows, size facilities, and evaluate the deployment for alternative energy sources that require locomotive powertrains to be converted and new refueling infrastructure to be deployed. The NUFRIEND Framework is an industry-oriented tool for simulating the deployment of new energy technologies across the US freight rail network. The framework provides a comprehensive network-level optimization and scenario simulation tool for decarbonizing the freight rail sector, addressing the uncertainties surrounding technological developments by supporting sensitivity analyses for different operational and technological parameters through a transparent and flexible input module. It offers practical alternatives to diesel locomotives and can be applied for any railroad considering the specific network structure and freight demand, outputting evaluation metrics for the associated emissions and costs relative to diesel operations. A number of relevant simulation scenarios were run and analyzed for key insights on the value of different alternative technologies for freight rail decarbonization. The project developments and findings have been presented at numerous conferences and events.

08 HYDROGEN↗

DOE OSTI2026

Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) (Final Technical Report)

The Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) project advances resilience science and engineering by addressing challenges in rural Kansas communities where aging infrastructure, extreme weather, and socioeconomic disparities heighten vulnerability to energy disruptions. Traditional approaches often focus on technical performance while overlooking community concerns and priorities. SAFER responds by integrating community perspectives with advanced analytical frameworks to create a holistic model for measuring and improving resilience. Project objectives included developing novel resilience metrics, advancing modeling frameworks that capture interdependencies across infrastructures, and embedding community-centric indicators directly into planning processes for distributed energy resources. The key technical innovations included the creation of self-organizing map (SOM)-based indices for objective resilience quantification, hetero-functional graph theory (HFGT) models linking power, water, transportation, and community assets, and graph neural network (GNN) tools for identifying critical nodes in complex systems. Community-centric energy planning was demonstrated through optimal siting and sizing of (photovoltaic) PV and battery storage, ensuring resilience enhancements also addressed energy burden and energy insecurity. SAFER engaged community partners in Dodge City and Ford County through surveys, focus groups, and workshops, generating more than 600 responses that established baseline measures of energy burden, financial insecurity, and willingness-to-pay to avoid outages. This data, organized in terms of a community capitals framework, informed the development of weighted reliability indices that better reflect community costs than traditional utility metrics. SAFER’s GNN-based critical node identification framework identified expert-labelled critical nodes with over 99% accuracy, while also uncovering additional functionalities essential for proactive resilience planning. The project’s models demonstrated that optimal PV and storage deployment could improve resilience indices by over 11 percent, with dispatch strategies further enhancing outcomes, confirming both the technical effectiveness and economic feasibility of these approaches. Through its combined emphasis on rigorous modeling, community-focused planning, and community engagement, SAFER advances the state of resilience research while delivering direct benefits to rural communities. The project provides tools, guidelines, and resilience heatmaps that help utilities, local governments, and residents better anticipate disruptions, prioritize investments, and strengthen the capacity to withstand and recover from energy-related hazards. Furthermore, the developed HFG and GNN frameworks are designed for transferability, allowing them to be adapted for resilience planning in other communities with minimal retraining. This inductive learning capability provides a scalable pathway to extend the SAFER project’s impact. Thus, creating a foundation for a nationally applicable model of infrastructure resilience. Additionally, the HFG can also be extended to include other FEMA community lifelines.

14 SOLAR ENERGY↗

DOE OSTI2017

The Multi-Mission Maximum Likelihood framework (3ML)

The age of multi-wavelength and multi-messenger astronomy has arrived and with it, new tools are needed to analyze data from multiple instruments properly and with ease. The Multi-Mission Maximum Likelihood framework (3ML) provides this functionality via the novel use of instrument plugins which allow for every instrument’s unique data to be treated independently with an appropriate likelihood. Under the 3ML framework, users can design plugins that handle instrument specific data routines transparently in the background. When multiple instruments are used together, their independent likelihoods are treated under a common minimization or Bayesian sampling framework. 3ML provides a multitude of minimization algorithm for maximum likelihood estimation (MLE) as well as several popular Bayesian posterior samplers. The entire framework is provided via a modern Python interface providing the user with a modern and easily transportable analysis framework well suited for modern astronomy. New models can be added easily. It is also possible to perform time-energy modeling. We present the framework and its main functionalities.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

An Overview of the Database Framework for GEM Detector under CMS Experiment at CERN

In this paper, we give an overview of the database framework which we have developed for the Gas Electron Multiplier (GEM) Detector at CERN. The GEM constitutes a powerful addition to the family of fast radiation detectors; originally developed for particle physics experiments, and has spawned a large number of developments and applications. The GEM database framework comprises four components. The first component is the database itself. There are three instances of the database which have been deployed. One is for the development purpose which contains test data, other one is for the integration purposes while the last one is for the production purpose which contains real data. The database further comprises various schemas and each schema consists of different tables in it. We use separate schemas for various types of tables. The second component of the database framework is called DB Loader. The DB Loader is used to load data into database. The data is prepared in the predefined format which is in the XML form. Then the xml file is copied into a spool area of the server in which DB loader is running. Once the file is copied, the DB Loader loads the file into the database. The loader returns appropriate status codes after performing database insertion/update operation. The status of the database operations is checked against the status code which is returned by the loader. The third component of the database framework comprises graphical user interface (GUI). This is a web-based interface which can be accessed from the internet. This interface is basically used for the detector construction and to perform various quality control tests on the detector and its components. The last component of the database framework is called online monitoring system (OMS). The OMS is data visualization framework for the various detectors of the CMS experiment at CERN. It is also used to display data for the GEM. It enables users to view and retrieve database contents without having to learn database specifics.

Imran, Muhammad↗