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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

MO-FG-202-04: Gantry-Resolved Linac QA for VMAT: A Comprehensive and Efficient System Using An Electronic Portal Imaging Device

Purpose: To automate gantry-resolved linear accelerator (linac) quality assurance (QA) for volumetric modulated arc therapy (VMAT) using an electronic portal imaging device (EPID). Methods: A QA system for VMAT was developed that uses an EPID, frame-grabber assembly and in-house developed image processing software. The system relies solely on the analysis of EPID image frames acquired without the presence of a phantom. Images were acquired at 8.41 frames per second using a frame grabber and ancillary acquisition computer. Each image frame was tagged with a gantry angle from the linac’s on-board gantry angle encoder. Arc-dynamic QA plans were designed to assess the performance of each individual linac component during VMAT. By analysing each image frame acquired during the QA deliveries the following eight machine performance characteristics were measured as a function of gantry angle: MLC positional accuracy, MLC speed constancy, MLC acceleration constancy, MLC-gantry synchronisation, beam profile constancy, dose rate constancy, gantry speed constancy, dose-gantry angle synchronisation and mechanical sag. All tests were performed on a Varian iX linear accelerator equipped with a 120 leaf Millennium MLC and an aS1000 EPID (Varian Medical Systems, Palo Alto, CA, USA). Results: Machine performance parameters were measured as a function of gantry angle using EPID imaging and compared to machine log files and the treatment plan. Data acquisition is currently underway at 3 centres, incorporating 7 treatment units, at 2 weekly measurement intervals. Conclusion: The proposed system can be applied for streamlined linac QA and commissioning for VMAT. The set of test plans developed can be used to assess the performance of each individual components of the treatment machine during VMAT deliveries as a function of gantry angle. The methodology does not require the setup of any additional phantom or measurement equipment and the analysis is fully automated to allow for regular routine testing.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Dynamic Multi-Sensor Multi-Mission Optimal Planning Tool

Remote sensing systems have firmly established a role in providing immense value to commercial industry, scientific exploration, and national security. Continued maturation of sensing technology has reduced the cost of deploying highly-capable sensors while at the same time increased reliance on the information these sensors can provide. The demand for time on these sensors is unlikely to diminish. Coordination of next-generation sensor systems, larger constellations of satellites, unmanned aerial vehicles, ground telescopes, etc. is prohibitively complex for existing heuristics-based scheduling techniques. The project was a two-year collaboration spanning multiple Sandia centers and included a partnership with Texas A&M University. We have developed algorithms and software for collection scheduling, remote sensor field-of-view pointing models, and bandwidth-constrained prioritization of sensor data. Our approach followed best practices from the operations research and computational geometry communities. These models provide several advantages over state of the art techniques. In particular, our approach is more flexible compared to heuristics that tightly couple models and solution techniques. First, our mixed-integer linear models afford a rigorous analysis so that sensor planners can quantitatively describe a schedule relative to the best possible. Optimal or near-optimal schedules can be produced with commercial solvers in operational run-times. These models can be modified and extended to incorporate different scheduling and resource constraints and objective function definitions. Further, we have extended these models to proactively schedule sensors under weather and ad hoc collection uncertainty. This approach stands in contrast to existing deterministic schedulers which assume a single future weather or ad hoc collection scenario. The field-of-view pointing algorithm produces a mosaic with the fewest number of images required to fully cover a region of interest. The bandwidth-constrained algorithms find the highest priority information that can be transmitted. All of these are based on mixed-integer linear programs so that, in the future, collection scheduling, field-of-view, and bandwidth prioritization can be combined into a single problem. Experiments conducted using the developed models, commercial solvers, and benchmark datasets have demonstrated that proactively scheduling against uncertainty regularly and significantly outperforms deterministic schedulers.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations

Background A unique archive of Big Data on Parkinson’s Disease is collected, managed and disseminated by the Parkinson’s Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson’s disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data–large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources–all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data. Methods and Findings Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson’s disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson's Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting. Conclusions Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson’s disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer’s, Huntington’s, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications.

Science & Technology - Other Topics↗
DOE OSTI2015

RELAP-7 Development Updates

RELAP-7 is a nuclear systems safety analysis code being developed at the Idaho National Laboratory, and is the next generation tool in the RELAP reactor safety/systems analysis application series. RELAP-7 development began in 2011 to support the Risk Informed Safety Margins Characterization (RISMC) Pathway of the Light Water Reactor Sustainability (LWRS) program. The overall design goal of RELAP-7 is to take advantage of the previous thirty years of advancements in computer architecture, software design, numerical methods, and physical models in order to provide capabilities needed for the RISMC methodology and to support nuclear power safety analysis. The code is being developed based on Idaho National Laboratory’s modern scientific software development framework – MOOSE (the Multi-Physics Object-Oriented Simulation Environment). The initial development goal of the RELAP-7 approach focused primarily on the development of an implicit algorithm capable of strong (nonlinear) coupling of the dependent hydrodynamic variables contained in the 1-D/2-D flow models with the various 0-D system reactor components that compose various boiling water reactor (BWR) and pressurized water reactor nuclear power plants (NPPs). During Fiscal Year (FY) 2015, the RELAP-7 code has been further improved with expanded capability to support boiling water reactor (BWR) and pressurized water reactor NPPs analysis. The accumulator model has been developed. The code has also been coupled with other MOOSE-based applications such as neutronics code RattleSnake and fuel performance code BISON to perform multiphysics analysis. A major design requirement for the implicit algorithm in RELAP-7 is that it is capable of second-order discretization accuracy in both space and time, which eliminates the traditional first-order approximation errors. The second-order temporal is achieved by a second-order backward temporal difference, and the one-dimensional second-order accurate spatial discretization is achieved with the Galerkin approximation of Lagrange finite elements. During FY-2015, we have done numerical verification work to verify that the RELAP-7 code indeed achieves 2nd-order accuracy in both time and space for single phase models at the system level.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

RELAP-7 Pressurizer Component Development Updates

RELAP-7 is a nuclear systems safety analysis code being developed at the Idaho National Laboratory (INL). RELAP-7 development began in 2011 to support the Risk Informed Safety Margins Characterization (RISMC) Pathway of the Light Water Reactor Sustainability (LWRS) program. The overall design goal of RELAP-7 is to take advantage of the previous thirty years of advancements in computer architecture, software design, numerical methods, and physical models in order to provide capabilities needed for the RISMC methodology and to support nuclear power safety analysis. The code is being developed based on Idaho National Laboratory’s modern scientific software development framework – MOOSE (the Multi-Physics Object-Oriented Simulation Environment). The initial development goal of the RELAP-7 approach focused primarily on the development of an implicit algorithm capable of strong (nonlinear) coupling of the dependent hydrodynamic variables contained in the 1-D/2-D flow models with the various 0-D system reactor components that compose various boiling water reactor (BWR) and pressurized water reactor nuclear power plants (NPPs). As part of the efforts to expand the capability for PWR simulation, an equilibrium single-region pressurizer model has been implemented in RELAP-7. The pressurizer component can simulate pressure and water level change through insurge, spray, and heating processes. Two simple tests – one for insurge process and another for outsurge process – have been reported to demonstrate and verify the functions of the pressurizer model. The typical single-phase PWR system model presented in the first RELAP-7 milestone report has been updated, as part of system level test for the new pressurizer model. The updated PWR system model with the pressurizer component can be used for more realistic transient simulations. The addition of the equilibrium single-region pressurizer model represents the first step of developing a suite of pressurizer models with different levels of fidelities. Non-equilibrium two-region two-zone model and non-equilibrium five-region three zone model will be implemented in the near future. Cross comparison and validation will be pursued when higher fidelity pressurizer models are completed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Vision and Development of a Design, Implementation, and Verification Automation (DIVA) Software Platform for DNA Construction

Abstract DNA construction, while a prerequisite to many biological endeavors, is often a time-consuming distraction from an individual’s primary research objectives. We envisioned that with the right software infrastructure and cultural mindset, a single person could execute in parallel the batched DNA construction tasks of an entire research institute, at scales realizing efficiency gains through process and laboratory automation. In pursuit of this vision, we developed the Design, Implementation, and Verification Automation (DIVA) software platform. DIVA’s web interface enables researchers to design DNA constructs (using visual biological computer-aided design tools and biological parts repositories), submit designs for construction to dedicated staff, and track DNA construction as it progresses. DIVA supports the dedicated staff through the DNA construction process and records both successful and unsuccessful attempts toward improving the overall process. The platform is publicly available at public-diva.jbei.org and its open-source code through github.com/JBEI/DIVA.

Plahar, Hector [DOE Agile BioFoundry , , ,; DOE Jo↗
DOE OSTI2018

Final Report to the National Energy Technology Laboratory on FY14- FY15 Cooperative Research with the Consortium for Electric Reliability Technology Solutions

The Power System Engineering Research Center (PSERC) engages in technological, market, and policy research for an efficient, secure, resilient, adaptable, and economic U.S. electric power system. PSERC, as a founding partner of the Consortium for Electric Reliability Technology Solutions (CERTS), conducted a multi-year program of research for U.S. Department of Energy (DOE) Office of Electricity Delivery and Energy Reliability (OE) to develop new methods, tools, and technologies to protect and enhance the reliability and efficiency of the U.S. electric power system as competitive electricity market structures evolve, and as the grid moves toward wide-scale use of decentralized generation (such as renewable energy sources) and demand-response programs. Phase I of OE’s funding for PSERC, under cooperative agreement DE-FC26-09NT43321, started in fiscal year (FY) 2009 and ended in FY2013. It was administered by DOE’s National Energy Technology Laboratory (NETL) through a cooperative agreement with Arizona State University (ASU). ASU provided sub-awards to the participating PSERC universities. This document is PSERC’s final report to NETL on the activities for OE, conducted through CERTS, from September 2015 through September 2017 utilizing FY 2014 to FY 2015 funding under cooperative agreement DE-OE0000670. PSERC is a thirteen-university consortium with over 30 industry members. Since 1996, PSERC has been engaged in research and education efforts with the mission of “empowering minds to engineer the future electric energy system.” Its work is focused on achieving: • An efficient, secure, resilient, adaptable, and economic electric power infrastructure serving society • A new generation of educated technical professionals in electric power • Knowledgeable decision-makers on critical energy policy issues • Sustained, quality university programs in electric power engineering. PSERC core research is funded by industry, with a budget supporting approximately 30 principal investigators and some 70 graduate students and other researchers. Its researchers are multi-disciplinary, conducting research in three principal areas: power systems, power markets and policy, and transmission and distribution technologies. The research is collaborative; each project involves researchers typically at two universities working with industry advisors who have expressed interest in the project. Examples of topics for recent PSERC research projects include grid integration of renewables and energy storage, new tools for taking advantage of increased penetration of real-time system measurements, advanced system protection methods to maintain grid reliability, and risk and reliability assessment of increasingly complex cyber-enabled power systems. A PSERC’s objective is to proactively address the technical and policy challenges of U.S. electric power systems. To achieve this objective, PSERC works with CERTS to conduct technical research on advanced applications and investigate the design of fair and transparent electricity markets; these research topics align with CERTS research areas 1 and 2: Real-time Grid Reliability Management (Area 1), and Reliability and Markets (Area 2). The CERTS research areas overlap with the PSERC research stems: Power Systems, Power Markets, and Transmission and Distribution Technologies, as described on the PSERC website (see http://www.pserc.org/research/research_program.aspx). The performers were with Arizona State University (ASU), Cornell University (CU), University of California at Berkeley (UCB), and University of Illinois at Urbana-Champaign (UIUC). PSERC research activities in the area of reliability and markets focused on electric market and power policy analyses. The resulting studies suggest ways to frame best practices using organized markets for managing U.S. grid assets reliably and to identify highest priority areas for improvement. PSERC research activities in the area of advanced applications focused on mid- to long-term software research and development, with anticipated outcomes that move innovative ideas toward real-world application. Under the CERTS research area of Real-time Grid Reliability Management, PSERC has been focused on Advanced Applications Research and Development (AARD), a subgroup of activities that works to develop advanced applications and tools to more effectively operate the electricity delivery system, by enabling advanced analysis, visualization, monitoring and alarming, and decision support capabilities for grid operators.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

The SCEC Unified Community Velocity Model Software Framework

Crustal seismic-velocity models and datasets play a key role in regional 3D numerical earthquake ground-motion simulation, full waveform tomography, and modern physics-based probabilistic earthquake-hazard analysis, as well as in other related fields, including geophysics and earthquake engineering. Most of these models and datasets, often collectively identified as Community Velocity Models (CVMs), synthesize information from multiple sources and are delivered to users in variable formats, including computer applications that allow for interactive querying of material properties, namely P- and S-wave velocities and density ρ. Computational users often require massive and repetitive access to velocity models and datasets, and such access is often unpractical and difficult due to a lack of standardized methods and procedures. To overcome these issues and to facilitate access by the community to these models, the Southern California Earthquake Center developed the Unified CVM (UCVM) software framework, an open-source collection of tools that enables users to access one or more seismic-velocity models, while providing a standard query interface. Here, we describe the research challenges that motivated the development of UCVM, its software design, development approach, and basic capabilities, as well as a few examples of seismic-modeling applications that use UCVM.

Geochemistry & Geophysics↗
DOE OSTI2020

Electronic structure with direct diagonalization on a D-wave quantum annealer

Quantum chemistry is regarded to be one of the first disciplines that will be revolutionized by quantum computing. Although universal quantum computers of practical scale may be years away, various approaches are currently being pursued to solve quantum chemistry problems on near-term gate-based quantum computers and quantum annealers by developing the appropriate algorithm and software base. This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer. The approach is based on the matrix formulation, efficiently uses qubit resources based on a power-of-two encoding scheme and is hardware-dominant relying on only one classically optimized parameter. We demonstrate the use of D-Wave hardware for obtaining ground and excited electronic states across a variety of small molecular systems. The approach can be adapted for use by a vast majority of electronic structure methods currently implemented in conventional quantum-chemical packages. The results of this work will encourage further development of software such as qbsolv which has promising applications in emerging quantum information processing hardware and has expectation to address large and complex optimization problems intractable for classical computers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Polyhedral approximation in mixed-integer convex optimization

Generalizing both mixed-integer linear optimization and convex optimization, mixed-integer convex optimization possesses broad modeling power but has seen relatively few advances in general-purpose solvers in recent years. Here, we intend to provide a broadly accessible introduction to our recent work in developing algorithms and software for this problem class. Our approach is based on constructing polyhedral outer approximations of the convex constraints, resulting in a global solution by solving a finite number of mixed-integer linear and continuous convex subproblems. The key advance we present is to strengthen the polyhedral approximations by constructing them in a higher-dimensional space. In order to automate this extended formulation we rely on the algebraic modeling technique of disciplined convex programming (DCP), and for generality and ease of implementation we use conic representations of the convex constraints. Although our framework requires a manual translation of existing models into DCP form, after performing this transformation on the MINLPLIB2 benchmark library we were able to solve a number of unsolved instances and on many other instances achieve superior performance compared with state-of-the-art solvers like Bonmin, SCIP, and Artelys Knitro.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Empirical Correction of Crosstalk in a Low-Background Germanium γ–γ Analysis System

ABSTRACT The Pacific Northwest National Laboratory is currently developing a custom software suite capable of automating many of the tasks required to accurately analyze coincident signals within gamma spectrometer arrays. During the course of this work, significant crosstalk was identified in the energy determination for spectra collected with a new low-background intrinsic germanium (HPGe) array at PNNL. The HPGe array is designed for high detection efficiency, ultra-low-background performance, and sensitive gamma gamma coincidence detection. The first half of the array, a single cryostat containing 7 HPGe crystals, was recently installed into a new shallow underground laboratory facility. This update will present a brief review of the germanium array, describe the observed crosstalk, and present a straight-forward empirical correction that significantly reduces the impact of this crosstalk on the spectroscopic performance of the system.

coincidence↗
DOE OSTI2018

Automated Model Updating Effect on the Linear and Nonlinear Dynamic Responses of Historical Masonry Structures

Finite element models supply the simulation of the problems to obtain the structural behaviors under different loading conditions. The updating processes based on changing the uncertain parameters such as material properties, boundary conditions, meshing size etc. are made to create actual finite element models in order to represent the real conditions of the problems. This procedure can be made using two methods, namely manual model updating and global/local automatic model updating. The global/local automatic model updating procedures are very popular owing to the applicability to all kind of engineering structures, minimizing the differences nearly to zero, availability of damage localization and structural health monitoring. In this paper a historical masonry armory building selected as an application and its finite element analyses, experimental measurements and automated model updating processes are carried out. The finite element model of the structures was developed with ANSYS software and first three natural frequencies are attained. The experimental dynamic characteristics such as natural frequencies, damping ratios and mode shapes are obtained using ambient vibration method. The differences between experimental and numerical frequencies are minimized up to the range of 0–1%, by using local updating procedure. The linear and nonlinear seismic analyses of the building are conducted on initial and updated models. At the end of the analyses, maximum displacements, principal stresses and strains are given with detail. In addition, crack distributions diagrams are attained to validate the stresses accumulation points. It can be seen that there is an increasing trend in the displacement, stress and strain values after finite element model updating both linear and nonlinear analysis. In addition, linear and nonlinear analysis results are compared with each other to evaluate the nonlinear analysis effect. It is concluded that the displacement and strain values are increased after nonlinear analysis. But, there is not any agreement in stress values.

42 ENGINEERING↗
DOE OSTI2026

Westcott g factors extended to arbitrary neutron energy spectra

Westcott 𝑔 factors are used in Neutron Activation Analysis (NAA) and Prompt Gamma-ray Activation Analysis (PGAA) to evaluate the impact of non-1∕𝑣 behavior in the neutron-capture cross sections of certain nuclei on activation product yields. This non-1∕𝑣 behavior arises from the presence of neutron resonances in the neutron- capture cross sections that overlap with the source neutron spectrum at low (< 5 eV) energies. Historically, Westcott 𝑔 factors that have been cataloged for NAA and PGAA applications are the result of calculations that assume a Maxwellian neutron flux distribution with a given temperature. In this work, we use this approach with updated neutron-capture cross sections from the Evaluated Nuclear Data File, version VIII.1 (ENDF/B-VIII.1) to tabulate Westcott 𝑔 factor values for a broad range of Maxwellian distribution temperatures, comparing the results against currently-available 𝑔 factors from International Atomic Energy Agency tables and other sources. Here, it was discovered during this analysis that the use of guided thermal and cold-neutron beams at certain facilities necessitates an approach for evaluating Westcott 𝑔 factors based on arbitrary non-Maxwellian spectra. In this paper, we present an approach for calculating 𝑔 factors with user-specified neutron spectra, and we demonstrate these methods to obtain Westcott 𝑔-factors for guided- and cold-neutron beams at the Budapest Research Reactor and the Forschungsreaktor München II reactor. As part of this work, open-source software has been developed that can be used to perform these calculations for applications in PGAA and NAA experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Framework and algorithms for illustrative visualizations of time-varying flows on unstructured meshes

Photo- and physically realistic techniques are often insufficient for visualization of fluid flow simulations, especially for 3D and time-varying studies. Substantial research effort has been dedicated to the development of non-photorealistic and illustration-inspired visualization techniques for compact and intuitive presentation of such complex datasets. However, a great deal of work has been reproduced in this field, as many research groups have developed specialized visualization software. Additionally, interoperability between illustrative visualization software is limited due to diverse processing and rendering architectures employed in different studies. In this investigation, a framework for illustrative visualization is proposed, and implemented in MarmotViz, a ParaView plug-in, enabling its use on a variety of computing platforms with various data file formats and mesh geometries. Region-of-interest identification and feature-tracking algorithms incorporated into this tool are described. Implementations of multiple illustrative effect algorithms are also presented to demonstrate the use and flexibility of this framework. Here, by providing an integrated framework for illustrative visualization of CFD data, MarmotViz can serve as a valuable asset for the interpretation of simulations of ever-growing scale.

42 ENGINEERING↗
DOE OSTI2015

Definition and solution of a stochastic inverse problem for the Manning’s n parameter field in hydrodynamic models

The uncertainty in spatially heterogeneous Manning’s n fields is quantified using a novel formulation and numerical solution of stochastic inverse problems for physics-based models. The uncertainty is quantified in terms of a probability measure and the physics-based model considered here is the state-of-the-art ADCIRC model although the presented methodology applies to other hydrodynamic models. An accessible overview of the formulation and solution of the stochastic inverse problem in a mathematically rigorous framework based on measure theory is presented in this paper. Technical details that arise in practice by applying the framework to determine the Manning’s n parameter field in a shallow water equation model used for coastal hydrodynamics are presented and an efficient computational algorithm and open source software package are developed. A new notion of “condition” for the stochastic inverse problem is defined and analyzed as it relates to the computation of probabilities. Finally, this notion of condition is investigated to determine effective output quantities of interest of maximum water elevations to use for the inverse problem for the Manning’s n parameter and the effect on model predictions is analyzed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

PERCEPTIVE: an R shiny $\underline{p}$ipelin$\underline{e}$ for the p$\underline{r}$edi$\underline{c}$tion of $\underline{ep}$igenetic modula$\underline{t}$ors $\underline{i}$n no$\underline{v}$el sp$\underline{e}$cies

Epigenetic processes are central to regulating gene expression, genome stability, and metabolic function across the tree of life; yet, their roles remain underexplored in microalgae, especially as new species continue to be identified and characterized. This is likely due to the cumbersome nature and species-dependent attributes of epigenetic wet-lab methodologies, which preclude the rapid identification of epigenetic modifications and modulators. However, there is high conservation of epigenetic processes from budding yeast to humans; in many cases, one may infer how behavior and function are epigenetically regulated in novel species by identifying epigenetic modulators, or the proteins responsible for conferring epigenetic modifications. Here, to this end, we have developed a graphical software package, titled PERCEPTIVE (pipeline for the prediction of epigenetic modulators in novel species). This platform solely uses the genomic sequence of an algal species, and preexisting information from other model organisms, to predict the epigenetic modulators and associated modifications in algae. Predictions are presented to the user in a graphical interface, which provides literature-based interpretation of results, enabling users to quickly understand potential epigenetic processes in their algal species of interest and plan follow-up experiments. To test PERCEPTIVE, we predicted epigenetic modulators in several feedstock candidate algae species. To validate these predictions, wet-lab studies were performed, including mass spectrometry; these results underscore the high accuracy of PERCEPTIVE predictions. Overall, PERCEPTIVE represents a powerful in silico tool for the research and manipulation of algal species, which does not require a priori knowledge of epigenetics and is accessible to a broad set of investigators.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Griffin: A MOOSE-based reactor physics application for multiphysics simulation of advanced nuclear reactors

Griffin is a Multiphysics Object-Oriented Simulation Environment (MOOSE) based reactor physics application for multiphysics simulations of advanced reactor designs jointly developed by Idaho National Laboratory and Argonne National Laboratory. This paper summarizes the motivation, significance, architecture, design, and features of Griffin. Griffin offers flexible and extensible features to address the challenges associated with advanced reactor designs. These features range from fundamental particle transport to specific reactor physics tasks. The features cover a wide range including on-the-fly and traditional two-step cross-section generation methods, steady-state and transient transport solvers suitable for both heterogeneous and homogeneous models, high-fidelity depletion where thousands of isotopes can be tracked and low-fidelity depletion characterized by burnup, etc. The most fundamental aspect that sets Griffin apart from other reactor analysis codes is that it is developed based on the MOOSE framework. A modular development approach is strongly enforced, with multiphysics being an essential element considered since the beginning of Griffin’s development. Griffin links various MOOSE physics modules and couples to other MOOSE-based applications and non-MOOSE-based applications for multiphyiscs simulations. Griffin includes three modules: ISOXML for preparing and managing multigroup cross sections, radiation transport for solving the neutron transport equation, and reactor analysis for user-oriented reactor physics analysis functionalities. Griffin uses various finite element methods for spatial discretization, multigroup approximation for energy discretization and discrete ordinates method, spherical harmonics expansion method, and diffusion approximation for streaming direction discretization to solve the neutron transport equation. Griffin’s flexibility is evidenced through Griffin’s various applications to fast reactor, high-temperature reactor, pebble bed reactor, molten salt reactor, and microreactor designs. Griffin development follows the software quality assurance procedure for MOOSE-based applications and with software requirements consistent with the ASME NQA-1 standard. Griffin has been adopted into the reactor analysis system for the U.S. NRC and is in use at U.S. companies, universities and national laboratories.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Technical and economic feasibility of solar flat-plate collector thermal energy systems for small and medium manufacturers

Manufacturing industries require process heating for a wide variety of operations, such as drying, cooking, washing, and sterilizing. Studies of industrial energy consumption have shown that more than 50% of industrial heating demand is in the low (<60 °C), medium (60-150 °C), and medium-high (<250 °C) temperature ranges. A significant fraction of this low temperature thermal energy could be generated by commercially available solar flat-plate collector thermal energy systems, which would reduce the use of fossil fuels, energy cost volatility, and carbon emissions. However, manufacturing industries have not been able to take advantage of solar flat-plate collector systems for two main reasons: First, information is not readily available about when and what flat-plate collector system sizes can be economically applied and, second, manufacturers often do not have appropriate personnel or analysis tools to make decisions about installation of solar flat-plate collector systems. The goal of this research is to provide design decision support to small- and medium-sized manufacturers in conducting feasibility analysis for the application of solar flat-plate collector systems in their operations. The $\bar{φ}, f$-chart method and modified $\bar{φ}, f$-chart design method are used to predict the amount of energy collected by solar flat-plate collectors. A simplified model is proposed to assess the cost of solar flat-plate collector energy systems. To demonstrate the presented feasibility analysis methodology, a decision support software application is developed. What-if scenarios are presented for several geographic locations of varying climates. A sensitivity analysis of savings-to-investment ratio (SIR) was performed by changing selected input parameters by ±30% from the base case. It was found that SIR is sensitive to natural gas price, followed by collector area dependent cost, discount rate, cost of the auxiliary gas heater, and cost of the preheating system.

14 SOLAR ENERGY↗