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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Leveraging NREL's ResStock & ComStock Dataset to Evaluate Building Stock Electrification: Preprint

Residential and commercial buildings accounted for 40% of U.S. energy consumption in 2022 and represent a significant opportunity for decarbonization through energy efficiency and electrification, and for grid planning. Building stock energy modeling is a powerful tool that can evaluate what-if scenarios as utilities, municipalities, policymakers, building owners and others work towards equitable building decarbonization and climate goals. This presentation will highlight several high-impact use cases of the National Renewable Energy Laboratory (NREL)'s highly granular, bottom-up building stock energy modeling tools, ResStock and ComStock. These use cases cover a wide range of project scale, from neighborhood electrification analysis and municipality long-term energy planning, to state energy code development and national policy evaluation. This presentation will showcase specific real-world applications for which ResStock and ComStock have been utilized across the country, including California codes and standards cost-effectiveness analysis, New York City affordable housing electrification cost gap analysis, and California targeted electrification and gas decommissioning analysis. For each use case, this presentation will illustrate how ResStock and ComStock played a crucial role in accurately characterizing regional building stocks, providing discrete and aggregated end-use load shapes, and calculating lifecycle consumption, emissions, and costs for a variety of building electrification strategies and scenarios. Finally, this presentation will demonstrate how the data provided by ResStock and ComStock can help unlock significant outcomes for these use cases, including but not limited to, customer bill impact, incentive and program design, and energy equity analyses.

building stock modeling↗
DOE OSTI2020

Tighter reformulations using classical Dawson and Sankoff bounds for approximating two-stage chance-constrained programs

We extend and improve recent results given by Singh and Watson on using classical bounds on the union of sets in a chance-constrained optimization problem. Specifically, we revisit the so-called Dawson and Sankoff bound that provided one of the best approximations of a chance constraint in the previous analysis. Next, we show that our work is a generalization of the previous work, and in fact the inequality employed previously is a very relaxed approximation with assumptions that do not generally hold. Computational results demonstrate on average over a 43% improvement in the bounds. As a byproduct, we provide an exact reformulation of the floor function in optimization models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Buffer-IPyC separation process in TRISO fuel particles simulated with Bison code

During High Temperature Gas-cooled Reactor (HTGR) operation, tristructural isotropic (TRISO) coated-particle fuel undergoes irradiation-induced changes in morphology and thermomechanical properties. Experimental results from the Advanced Gas Reactor (AGR) Fuel Development and Qualification Program show, among other things, the mechanism of gap formation between the buffer and inner pyrolytic carbon (IPyC) layers, which could be explored further via computational simulations using the Bison code. Two simulation models were developed, the debonding restricted model, where no gap formation between buffer and IPyC layers is permitted, and the debonding enabled model, where the gap between those layers is created. The inputs of the simulated models are based on the irradiation conditions from the AGR-1 experiment. The research included simulations on spherical and aspherical fuel types. Under the specific temperatures and fluences of the AGR-1 irradiation experiment, and the Bison simulations, it was concluded that the most common scenario is a gap formation along the buffer-IPyC interface, while the least possible scenario is the situation where there is no gap formation at the buffer-IPyC junction. The computational results confirmed that the sphericity of the fuel influences the thickness of the gap that occurs at the buffer-IPyC junction, in a way that with increasing aspect ratio the gap thickness increases. The results obtained for spherical and aspherical fuel are nearly identical. Finally, performed simulations match conclusions observed from the AGR-1 experiment, which as such shows that the Bison code is a good computational method for simulating the TRISO fuel. Future simulations will include the validation of performed research and comparison of the results between Bison and PARFUME codes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Systematically testing singlet models for $(g - 2)_{μ}$

We comprehensively study all viable new-physics scenarios that resolve the muon $(g - 2)_{μ}$ anomaly with only Standard Model singlet particles coupled to muons via renormalizable interactions. Since such models are only viable in the MeV–TeV mass range and require sizable muon couplings, they predict abundant accelerator production through the same interaction that resolves the anomaly. We find that a combination of fixed-target $(\text {NA64}μ, M^{3})$, B-factory (BABAR, Belle II), and collider (LHC, muon collider) searches can cover nearly all viable singlets scenarios, independently of their decay modes. In particular, future muon collider searches offer the only certain test of singlets above the GeV scale, covering all higher masses up to the TeV-scale unitarity limit for these models. Intriguingly, we find that $ \mathcal{O}\left(100\ \mathrm{GeV}\right) $ muon colliders may yield better coverage for GeV-scale singlets compared to TeV-scale concepts, which has important implications for the starting center-of-mass energy of a staged muon collider program.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Random field optimization

Herein we present a new modeling paradigm for optimization that we call random field optimization. Random fields are a powerful modeling abstraction that aims to capture the behavior of random variables that live on infinite-dimensional spaces (e.g., space and time) such as stochastic processes (e.g., time series, Gaussian processes, and Markov processes), random matrices, and random spatial fields. This paradigm involves sophisticated mathematical objects (e.g., stochastic differential equations and space-time kernel functions) and has been widely used in neuroscience, geoscience, physics, civil engineering, and computer graphics. Despite of this, however, random fields have seen limited use in optimization; specifically, existing optimization paradigms that involve uncertainty (e.g., stochastic programming and robust optimization) mostly focus on the use of finite random variables. This trend is rapidly changing with the advent of statistical optimization (e.g., Bayesian optimization) and multi-scale optimization (e.g., integration of molecular sciences and process engineering). Our work extends a recently-proposed abstraction for infinite-dimensional optimization problems by capturing more general uncertainty representations. Moreover, we discuss solution paradigms for this new class of problems based on finite transformations and sampling, and identify open questions and challenges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

U.S.-China Clean Energy Research Center Building Energy Efficiency (CERC-BEE) Open-Source Retrofit Targeting Tool (CRADA FP00007338 Final Report)

To increase the cost-saving energy and carbon dioxide (CO 2 ) emissions reductions in buildings and portfolios at the scale and speed necessary to limit climate change, researchers at LBNL and Johnson Controls (JCI) developed the Building Efficiency Targeting Tool for Energy Retrofits (BETTER). BETTER is a software tool that consists of three components: (1) the BETTER analytical engine source code (which was developed with intellectual property provided by JCI under CRADA FP00007338); (2) the BETTER web application, developed by LBNL and McQuillen Interactive Pty. Ltd; and (3) the BETTER application programming interface (API), also developed by LBNL and McQuillen Interactive Pty. Ltd. BETTER enables building and portfolio owners, managers, and service providers worldwide to quickly, easily identify cost-saving energy efficiency retrofits in existing buildings and portfolios without expensive site visits or complex modeling. With minimal data input, the tool benchmarks a building’s electric and fossil energy usage against peers; quantifies energy, cost and greenhouse gas (GHG) emission reduction potentials at the building and portfolio levels; and recommends energy efficiency measures to decarbonize and electrify buildings and portfolios, targeting specific energy savings levels. No other tool so comprehensively analyzes buildings and portfolios with such ease. If fully implemented, it is estimated that BETTER could help reduce emissions equivalent to planting 1.3 billion trees globally by 2030. Moreover, an additional 50-75% of embodied GHG emissions could be avoided in each case where BETTER results in a building being retrofitted instead of demolished and replaced, providing substantial additional decarbonization benefits for the buildings sector. BETTER has garnered multiple awards and avid interest from investors. In 2020, it earned a R&D 100 Award for innovation and a LBNL Director’s Award for Technology Transfer. In 2021, BETTER was named an EarthX E-Capital Summit Climate Tech Prize semi-finalist

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

From PeleC to PeleACC, to PeleC++

PeleC is an Exascale Computing Project application for simulating compressible combustion in complex geometries. It has been built on top of the popular AMReX library. In the beginning of the Exascale Computing Project, PeleC was focused on KNL. It uses a mixture of C++, C, and kernels written in Fortran to obtain performance by focusing on vectorization. Recently we have taken two approaches in deciding PeleC's future for obtaining performance on exascale GPU machines. In the first programming model, we decorated the Fortran kernels with OpenACC directives. This expedited our ability to run at large scales on Summit's GPUs, where we achieved a significant speedup over the CPUs on Summit. The second programming model involved rewriting the Fortran kernels in C++ and using AMReX's Kokkos-like lambda abstractions for running on the GPU. This resulted in similar speedups on Summit's GPUs over merely utilizing the CPUs. Both approaches involved AMReX's management of memory transfers between the device and host. In this work, we compare and contrast the benefits and pitfalls to both programming approaches regarding performance, performance portability, and productivity. We also discuss advantages we have found in taking the time to modernize our code and why have chosen a specific pathway to prepare our code for the future DOE exascale machines.

exascale computing↗
DOE OSTI2020

NEAMS Workbench Status and Capabilities

The mission of the US Department of Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program is to develop, apply, deploy, and support state-of-the-art predictive modeling and simulation tools for the design and analysis of current and future nuclear energy systems. This is accomplished by using computing architectures that range from laptops to leadership-class facilities. The NEAMS Workbench is a new initiative that will facilitate the transition from conventional tools to highfidelity tools by providing a common user interface for model creation, review, execution, output review, and visualization for integrated codes. The Workbench can use common user input, including engineering-scale specifications that are expanded into application-specific input requirements through the use of customizable templates. The templating process can enable multifidelity analysis of a system from a common set of input data. Additionally, the common user input processor can provide an enhanced alternative application input that provides additional conveniences compared with native input, especially for legacy codes. Expansion of the integrated codes and application templates available in the Workbench will broaden the NEAMS user community and will facilitate system analysis and design. Current and planned capabilities of the NEAMS Workbench are detailed herein.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Paving the Way for Net Zero Energy Codes through Performance Based Approaches

The prescriptive path is the most widely used approach for commercial code compliance in the United States. However, to achieve near-term net zero energy performance that many stakeholders in the buildings industry desire, it is clear that energy codes will need to transition from prescriptive to performance-based approaches. However, that transition is not without potential pitfalls. Among the concerns expressed by stakeholders are added complexity and cost, lack of confidence in energy modeling results, gamesmanship on the part of applicants, lack or qualified reviewers, challenges in promoting de-carbonization, and the inequity of trading long lived envelope efficiency for short lived measures such as building controls. This paper will discuss multiple approaches being evaluated and implemented by Pacific Northwest National Laboratory to improve the usability of the performance-based approach for both code compliance and beyond code programs to meet aggressive energy savings targets. A 'system performance' approach provides a simpler approach compared to whole building performance, while keeping tradeoffs limited to specific building systems. A simplified whole-building performance-based approach for small commercial buildings can cost effectively achieve deeper savings for buildings that typically follow the prescriptive compliance approach. Improved reporting and verification processes applied to the traditional performance path can provide greater confidence in simulation results and facilitate adoption of performance-based approaches. Prescriptive packages provide prescriptive solutions while targeting specific performance thresholds. This paper discusses the progress being made with each of these approaches and provides examples of their implementation in energy codes and beyond code programs.

Performance Based Codes, Appendix G, PRM, TSPR, Sy↗
DOE OSTI2020

Validation of Continuous-Energy ENDF/B-VIII.0 16 O, 56 Fe, and 63,65 Cu Cross Sections for Nuclear Criticality Safety Applications

Recently completed cross-section evaluations sponsored in part by the Nuclear Criticality Safety Program were incorporated into the 2018 release of the ENDF/B-VIII.0 cross-section library. Evaluated isotopes of interest to the nuclear data and criticality safety community include 16 O, 56 Fe, and 63,65 Cu. For performance validation, benchmark models defined in the International Criticality Safety Benchmark Evaluation Project Handbook were selected based on energy-integrated k eff sensitivities to total cross sections of interest and compared with experimental values. Of the 102 benchmark configurations that were utilized, 63 are sensitive to 16 O, 32 sensitive to 63,65 Cu, and 25 sensitive to 56 Fe. Selected benchmarks were modeled in SCALE 6.2.3 Criticality Safety Analysis Sequence (CSAS) continuous-energy Monte Carlo k eff calculations with ENDF/B-VII.1, with a hybrid ENDF/B-VII.1 with ENDF/B-VIII.0 data substituted for individual isotopes of interest, and with ENDF/B-VIII.0. ENDF/B-VIII.0 showed improved agreement with experimental k eff for 56 Fe, 63 Cu, elemental copper, and full library substitution while producing lessened agreement for 16 O and 65 Cu. Furthermore, with full library and isotope-specific ENDF/B-VIII.0 performance, a best-case ENDF library was formed by excluding underperforming isotopes’ ENDF/B-VIII.0 data, reverting 16 O and 65 Cu cross sections to ENDF/B-VII.1. This resulted in the average relative deviation between calculated and experimental data improving from 1.45σ for the ENDF/B-VIII.0 library to 1.32σ for the best-case library, relative to benchmark uncertainty.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

MILK : a Python scripting interface to MAUD for automation of Rietveld analysis

Modern diffraction experiments ( e.g. in situ parametric studies) present scientists with many diffraction patterns to analyze. Interactive analyses via graphical user interfaces tend to slow down obtaining quantitative results such as lattice parameters and phase fractions. Furthermore, Rietveld refinement strategies ( i.e. the parameter turn-on-off sequences) tend to be instrument specific or even specific to a given dataset, such that selection of strategies can become a bottleneck for efficient data analysis. Managing multi-histogram datasets such as from multi-bank neutron diffractometers or caked 2D synchrotron data presents additional challenges due to the large number of histogram-specific parameters. To overcome these challenges in the Rietveld software Material Analysis Using Diffraction ( MAUD ), the MAUD Interface Language Kit ( MILK ) is developed along with an updated text batch interface for MAUD . The open-source software MILK is computer-platform independent and is packaged as a Python library that interfaces with MAUD . Using MILK , model selection ( e.g. various texture or peak-broadening models), Rietveld parameter manipulation and distributed parallel batch computing can be performed through a high-level Python interface. A high-level interface enables analysis workflows to be easily programmed, shared and applied to large datasets, and external tools to be integrated with MAUD . Through modification to the MAUD batch interface, plot and data exports have been improved. The resulting hierarchical folders from Rietveld refinements with MILK are compatible with Cinema: Debye–Scherrer , a tool for visualizing and inspecting the results of multi-parameter analyses of large quantities of diffraction data. In this manuscript, the combined Python scripting and visualization capability of MILK is demonstrated with a quantitative texture and phase analysis of data collected at the HIPPO neutron diffractometer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Oxy-Combustion System Process Optimization

The overall objective of this work is to develop a new chemical absorbent-based, high pressure, CO 2 purification system to remove the residual oxygen that currently contaminates the recovered CO 2 , and to optimize the Pressurized Oxy-Combustion (POxC) process to minimize the Cost of Electricity (COE) generated in this advanced combustion process. TDA developed and validated the performance of the oxygen removal system for CO 2 purification. In collaboration with the Advanced Power & Energy Program (APEP) of University of California, Irvine (UCI), we optimized the POxC process, including thermal management, heat integration, and power cycle optimization using process design and modeling supported with Aspen Plus® process simulations. The techno-economic analysis results indicate that the pressurized oxycombustion coal power plant with Ion-transport membrane (ITM) air separation unit (ASU) (Case 2 – 30.55%) does not show an advantage over a cryogenic ASU (Case 1 – 31.24%) while TDA’s sorption-based ASU (Case 4 - 32.61%) shows a significant advantage over the cryogenic ASU (Case 1 – 331.24%). The specific plant costs show a wide range with a low of $2544/kW for Case 11C (TDA ASU, co-sequestering the SO x , and ultra-supercritical steam cycle) to a high of $2975/kW for Case 12A (cryogenic ASU and sCO 2 cycle). In general, the ITM ASU based cases have lower specific plant costs than the corresponding cryogenic ASU based cases while the TDA ASU based cases show the lowest specific plant costs. The main reason for lowering these costs is the higher overall plant thermal efficiency which decreases the plant cost on a $ per kW basis. Next comparing the cases with different power cycle working fluid conditions in terms of temperature and pressure while all utilizing steam, similar trends as the plant costs may be observed. However, with the supercritical CO 2 (sCO 2 ) cycle, the increase in thermal efficiency of the sCO 2 cycle was not able to offset its increase in plant cost making the plant costs higher than those of the corresponding steam cycle cases. The Cost of Electricity (COE) again shows similar trends as the specific plant costs. The COE for Case 11C at $110.1/MWh is also the lowest, but among all cases that do not co-sequester the SO x , Case 12C (TDA ASU and sCO 2 cycle) has the lowest COE at $\$$117.5/MWh while the highest is for Case 8A (cryogenic ASU and supercritical steam cycle) at $130.4/MWh.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

IACMI Project 4.10: Blade Finishing Automation (Final Report)

IACMI Project 4.10, the Innovative Wind Turbine Blade Finishing with Advanced Automated Technology project, performed research addressing the challenges and opportunities associated with automating wind turbine blade finishing operations, including blade trimming and grinding. These activities, developed and conducted in close cooperation with wind industry partners, included: • The development of new automation technology and systems; • The integration of automation technology and components; • The improvements of sensor technology and their deployment in automation systems • The elimination of extensive manual programing for the automation of wind blade finishing; • The rethinking of the approach to specific work tasks for automation systems; • The demonstration at scale of innovative wind turbine blade finishing automations systems. The IACMI Wind TA team worked with GE Renewable Energy and LM Wind Power to establish the metrics of the automated blade finishing system developed in the project. In addition, the project team helped to integrate the advanced automated systems into wind turbine blade production facilities. This project was divided into two phases. Phase I provided project management, preliminary techno-economic model development, early US wind turbine industry outreach, and early research into robotic solutions for automated wind blade finishing. This second phase continued with project management, as well as final techno-economic model development, advanced research into robotic solutions for automated wind blade finishing, as well as specifications and procurement of a robotic system for automated wind blade finishing. The 4.10 project leveraged the capabilities and facilities established through the Institute for Advanced Composites Manufacturing Innovation (IACMI), the Colorado Office of Economic Development and International Trade (OEDIT) and the National Renewable Energy Laboratory (NREL). The IACMI Wind Technology Area (TA) designed and prepared for deployment a robotic automated wind turbine blade finishing system at the NREL/IACMI Composites Manufacturing Education and Technology (CoMET) facility. The results of this project led to follow-on funding to build upon and expand research in automated wind turbine blade finishing.

17 WIND ENERGY↗
DOE OSTI2025

IM3 Open Source Data Center Atlas

IM3 Open Source Data Center Atlas Description This dataset contains locations of existing data center facilities in the United States. Data center locations were derived from OpenStreetMap (OSM), a crowd-sourced database. Data points from OSM are processed in various ways to determine additional variables provided in the data including: facility area (square feet), associated US county, and US state. This dataset can be used to identify areas of concentrated data center development and inform government and private sector planning strategies for future buildout of data centers and the infrastructure necessary to support it. Usage Notes Validation of OSM-derived data center locations is an ongoing development under the IM3 project, and the database will be updated as new information becomes available. In some instances, both the data center area (e.g., campus) and individual data center buildings are included as overlapping areas in the database. Both values are retained. Data center points, buildings, and campus areas are provided as separate layers in the downloadable data package. Note that data items are not necessarily complete across layers. That is, a specific data center may only be present as a single point geometry in the "point" layer while other data centers are represented in both the campus and building layers. In some cases, data center campuses and/or buildings straddle a county boundary line. Mappings to both counties are retained in the database as separate rows. These data rows will have the same data center id information, but each will have different county information. Crowd-sourced data, by nature, relies on individuals and communities to provide information. As a result, some data may be missing where it has not yet been reported. As we collect information on additional data center locations and as OSM receives additional contributions, the database will be updated to capture additional data points not yet shown. Technical Information Data is available for download under the following formats: GeoPackage (GPKG) CSV Geospatial data is provided in the WGS84 (EPSG:4326) coordinate reference system. The GeoPackage download contains the following layers. See usage notes for more information. "point" "building" "campus" The "point" layer includes all data from OSM that had POINT geometry type (i.e., individual coordinates). The "building" layer includes all OSM data that did not have POINT geometry and where the building tag in the OSM export was neither equal to "no" or null. Data that did not meet the "point" or "building" qualification was assumed to be a facility campus and included in the "campus" layer. The dataset contains the following parameters. Variables provided by OSM are labeled with (OSM-provided). id - unique identification number (OSM-provided with prefix of "node/", "relation/" and similar attributes removed) state - name of US state state_abb - two letter US state abbreviation state_id - state ID number county - name of US county county_id - county ID number ref - reference numbers or codes (OSM-provided) operator - the name of the company, corporation, or person in charge facility (OSM-provided) name - name of facility (OSM-provided) sqft - surface area of facility polygon, measured in square feet. Only available for "building" and "campus" layers lat - latitude of data centroid point lon - longitude of data centroid point type – represented spatial information. One of "point", "building", or "campus". geometry – POLYGON geometry of area footprint (in "campus" and "building" layers) or POINT geometry of locations (in "point" layer). This parameter is not included in the csv download. Attribution Data center locations were derived from OpenStreetMap, which is made available at openstreetmap.org under the Open Database License (ODbL). US state and county boundary information was collected from the US Census Bureau for the year 2024, which is made publicly available at https://www.census.gov/geographies/mapping-files.html Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License The IM3 Open Source Data Center Atlas is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall [Pacific Northwest National Labor↗
DOE OSTI2021

Estimating carrying capacity for juvenile salmon using quantile random forest models

Abstract Establishing robust methods and metrics to evaluate habitat quality is critical for the recovery of endangered Pacific salmonids ( Oncorhynchus spp.). A variety of modeling approaches are used for status and trend monitoring of anadromous species throughout the Pacific Northwest, USA, but current methods may fail to capture the complex relationship between fish and habitat and are often limited in predictive power beyond specific watersheds. Further, the focus on species distribution and abundance is not easily manipulated to predict carrying capacity and traditional stock‐recruitment analyses are reliant on long‐term data which are not always available. In this study, we developed a quantile random forest model to provide estimates of habitat carrying capacity for Chinook salmon ( O. tshawytscha ) parr during the summer months, at both the site and watershed scale. Quantile random forest models allow for the consideration of noisy data, correlated variables, and non‐linear relationships: common features in fish–habitat datasets. We leveraged Columbia Habitat Monitoring Program data to select habitat co‐variates and predict capacity at those sites. We also identified a set of globally available attributes to extrapolate capacity estimate predictions throughout wadeable streams within the Columbia River basin. Total capacity estimates for watersheds closely matched estimates from alternative fish productivity models. Carrying capacity estimates based on quantile random forest models, like those presented here, provide managers a framework to guide the identification, prioritization, and development of habitat rehabilitation actions to recover salmon populations.

See, Kevin E.↗
DOE OSTI2021

The ezSpectra suite: An easy‐to‐use toolkit for spectroscopy modeling

Abstract A molecule's spectrum encodes information about its structure and electronic properties. It is a unique fingerprint that can serve as a molecular ID. Quantum chemistry calculations provide key ingredients for interpreting spectra, but modeling the spectra rarely ends there; it requires additional steps that entail combined treatments of electronic and nuclear degrees of freedom and account for specifics of the experimental setup (light energy, polarization, averaging over molecular orientations, temperature, etc.). This Software Focus article describes the ezSpectra suite, which currently comprises two stand‐alone open‐source codes: ezFCF and ezDyson . ezFCF calculates Franck–Condon factors, which yield vibrational progressions for polyatomic molecules, within the double‐harmonic approximation. ezDyson calculates absolute cross‐sections for photodetachment/photoionization processes and photoelectron angular distributions using Dyson orbitals computed by a quantum chemistry program. This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods Theoretical and Physical Chemistry > Spectroscopy Software > Simulation Methods

Gozem, Samer↗
DOE OSTI2021

Advanced Simulation and Computing (FY22 Implementation Plan Rev 0)

The DOE National Nuclear Security Administration (NNSA) Stockpile Stewardship Program (SSP) is an integrated technical program for maintaining the safety, surety, and reliability of the U.S. nuclear stockpile. The SSP incorporates nuclear test data, computational modeling and simulation, and experimental facilities to advance understanding of nuclear weapons. The suite of data analyzed comes from activities including stockpile surveillance, experimental research, and development and engineering programs. This integrated national program requires the continued use of experimental facilities and the computational capabilities to support the SSP missions. These component parts, in addition to an appropriately scaled production capability, enable NNSA to support stockpile requirements. The ultimate goal of the SSP, and thus of the Advanced Simulation and Computing (ASC) Program, is to ensure that the U.S. maintains a safe, secure, and effective strategic deterrent. Specific work activities and scope contained in this Implementation Plan (IP) represent the full-year annual operating plan for FY22. The Initial IP, effective , should be consistent with the Department’s Base Table when operating under a Continuing Resolution (CR). The final IP, effective date TBD, is consistent with the final, enacted appropriation.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Supplement to M&V Guidelines: Measurement and Verification for Performance-Based Contracts Version 4.0

Measurement and verification (M&V) are key to determining and confirming the continued operation of, and savings associated with, performance-based contracts. The Federal Energy Management Program (FEMP) updates these guidelines to reflect the evolution of M&V to incorporate industry best practices and ensure that savings from these performance-based projects are properly planned, communicated, and realized. FEMP released M&V Guidelines: Measurement and Verification for Federal Energy Projects Version 3.0 in 2008. This document was developed with input from industry-government working groups, previous M&V guideline authors, and national laboratory subject matter experts. The document provided the methods and guidelines, with a focus on federal energy savings performance contracts (ESPCs). When FEMP released M&V Guidelines: Measurement and Verification for Performance-Based Contracts Version 4.0 in 2015, FEMP expanded the intended audience to include anyone (energy managers, procurement officers, and contractors) involved in implementing performance-based contracts. The streamlined M&V Guidelines Version 4.0 provided the procedures and guidelines needed to quantify savings resulting from installing energy-efficient equipment, water conservation, improved operation and maintenance (O&M), renewable energy, and cogeneration projects with a performance-based contract; however, it removed some of the associated details on topics such as developing regression models and sampling plans. The details and information provided in M&V Guidelines v. 3.0 are still pertinent to M&V for performance-based contracts. The objective is to keep FEMP M&V version 4.0 condensed, providing the reference document for specifying M&V methods and procedures and a resource for developing project-specific M&V plans; this supplement provides additional details. The content of this supplement has been updated to include pertinent revisions between M&V version 3.0 and M&V version 4.0 by providing details and updating the content to ensure relevancy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗