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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Reported Energy and Cost Savings from the DOE ESPC IDIQ Program: FY 2024

Energy Savings Performance Contracts (ESPCs) are a contractual mechanism that allow a federal agency to procure energy savings and facility improvements without upfront capital costs to reduce costs and enhance mission resiliency. ESPCs are covered under FAR Part 23.2, and 42 USC § 8287. Section 8287(a)(2)(A) of Title 42 of the U.S. Code requires that each energy savings performance contract (ESPC) undergo an annual energy audit, resulting in a separate audit report for every project. The objective of the present report is to compile and analyze all annual ESPC audit reports issued between October 1, 2023, and September 30, 2024, for projects awarded under Generations 1, 2, and 3 of DOE’s ESPC IDIQ contracts. During this period, 205 measurement and verification (M&V) reports were produced for 200 projects; the total number of reports exceeds the number of projects because some projects generated more than one report (for example, a few projects measure savings twice per year and produce two audit reports annually, each covering a different six-month period). By aggregating the results from these individual audits, the report determines the portfolio-wide realization rate of energy and cost savings for all active ESPC projects awarded under DOE’s IDIQ program.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

CNL Calandria Lifetime Extension Program: 1st Capsule Assembly

This report provides documentation and a summary of all 18 capsules assembled for the Canadian Nuclear Laboratories (CNL) Calandria Lifetime Extension Program. This is the first set of capsules in a series of two irradiation campaigns to provide data relevant to weldments in the calandria of Canada deuterium uranium (CANDU) reactors. This report provides documentation on capsule IDs, specimen IDs, and capsule locations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Optimization of wall assembly for DOE Efficient New Homes Program in HUD’s Climate Zones 2 and 3

Manufactured homes provide affordable housing for over 20 million Americans. The U.S. Department of Energy’s Efficient New Homes (ENH) program has introduced new energy certification requirements that manufacturers can optionally target to ensure outstanding levels of energy savings, comfort, health, and durability. In collaboration with Clayton Homes (under User Agreement (NN-23-1469)), Oak Ridge National Laboratory evaluated multiple wall assembly options to meet ENH requirements in HUD Climate Zones 2 and 3, with a target maximum cost premium of 5%. After analyzing thermal performance, cost, and manufacturability, insulated structural sheathing was identified as the most practical and effective solution. A full-scale manufactured home prototype demonstrated that this approach meets energy performance goals with minimal disruption to existing manufacturing processes and a cost premium of less than 10% with a path to further lower costs, offering a promising path forward for more efficient manufactured housing.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

A Joint Search for Muon Neutrino Disappearance with the Short-Baseline Neutrino Program Using the SPINE Deep Learning-Based Reconstruction Package

We present the status of a joint search for muon neutrino disappearance in the Booster Neutrino Beam at Fermilab using the Short-Baseline Neutrino (SBN) Program's two-detector configuration, SBND and ICARUS. Charged-current interactions consistent with muon neutrinos and containing only a muon and at least one proton in the final state are reconstructed and selected using the SPINE deep learning-based particle reconstruction package. To exploit proton multiplicity information and enhance sensitivity to modeling effects, the selected sample is partitioned into exclusive channels with exactly one reconstructed proton and with more than one reconstructed proton. Comprehensive systematic uncertainties from the neutrino flux, interaction, and detector response are incorporated into the analysis, and the coverage of these systematic uncertainty models is validated using data from both detectors, including checks of near-far consistency in key kinematic and topology-sensitive observables. This analysis is intended for inclusion in SBN's first oscillation result.

Mueller, Justin [Fermilab]↗
DOE OSTI2024

Exploiting Modern C++ for Portable Parallel Programming in Lattice QCD Applications

The evolution of ISO C++ standards increasingly serves the needs of scientific computing, offering potential benefits for developing portable applications. The recent revisions of C++ programming language, for instance, introduces a suite of algorithms capable of being executed on accelerators. Although this approach may not yield best performance, it can present a viable balance between code productivity and computational efficiency. In this report, we discuss the implementation of the HISQ operator utilizing a range of features from the C++17/20/23 standards and include an assessment of their performance.

Strelchenko, Alexei↗
DOE OSTI2024

The Deep Underground Neutrino Experiment (DUNE) Program

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino oscillation experiment aimed at determining the neutrino mass hierarchy and the CP-violating phase. The DUNE physics program also includes the detection of astrophysical neutrinos and the search for signatures beyond the Standard Model, such as nucleon decays. DUNE consists of a near detector complex located at Fermilab and four 17 kton Liquid Argon Time Projection Chamber (LArTPC) far detector modules to be built 1.5 km underground at SURF, approximately 1300 km away. The detectors are exposed to a wideband neutrino beam generated by a 1.2 MW proton beam with a planned upgrade to $>$ 2 MW. Two 770 ton LArTPCs (ProtoDUNEs) have been operated at CERN for over 2 years as a testbed for DUNE far detectors and have been optimized to take new cosmic and test-beam data in 2024-2025. The DUNE and ProtoDUNE experiments and physics goals, as well as recent progress and results, are presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Variational Quantum Algorithms for Semidefinite Programming

A semidefinite program (SDP) is a particular kind of convex optimization problem with applications in operations research, combinatorial optimization, quantum information science, and beyond. In this work, we propose variational quantum algorithms for approximately solving SDPs. For one class of SDPs, we provide a rigorous analysis of their convergence to approximate locally optimal solutions, under the assumption that they are weakly constrained (i.e., N$\gg$M, where N is the dimension of the input matrices and M is the number of constraints). We also provide algorithms for a more general class of SDPs that requires fewer assumptions. Finally, we numerically simulate our quantum algorithms for applications such as MaxCut, and the results of these simulations provide evidence that convergence still occurs in noisy settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Mapping Spiking Neural Networks to Heterogeneous Crossbar Architectures using Integer Linear Programming

Advances in novel hardware devices and architectures allow Spiking Neural Network (SNN) evaluation using ultra-low power, mixed-signal, memristor crossbar arrays. As individual network sizes quickly scale beyond the dimensional capabilities of single crossbars, networks must be mapped onto multiple crossbars. Crossbar sizes within modern Memristor Crossbar Architectures (MCAs) are determined predominately not by device technology but by network topology; more, smaller crossbars consume less area thanks to the high structural sparsity found in larger, brain-inspired SNNs. Motivated by continuing increases in SNN sparsity due to improvements in training methods, we propose utilizing heterogeneous crossbar sizes to further reduce area consumption. This approach was previously unachievable as prior compiler studies only explored solutions targeting homogeneous MCAs. Our work improves on the state-of-the-art by providing Integer Linear Programming (ILP) formulations supporting arbitrarily heterogeneous architectures. By modeling axonal interactions between neurons, our methods produce better mappings while removing inhibitive a priori knowledge requirements. We first show a 16.7-27.6% reduction in area consumption for square-crossbar homogeneous architectures. Then, we demonstrate 66.9-72.7% further reduction when using a reasonable configuration of heterogeneous crossbar dimensions. Next, we present a new optimization formulation capable of minimizing the number of inter-crossbar routes. When applied to solutions already near-optimal in area, an 11.9-26.4% routing reduction is observed without impacting area consumption. Finally, we present a profile-guided optimization capable of minimizing the number of runtime spikes between crossbars. Compared to the best-area-then-route optimized solutions, we observe a further 0.5-14.8% inter-crossbar spike reduction while requiring 1–3 orders of magnitude less solver time.

Pohl, Devin [ORNL] (ORCID:0009000040149027)↗
DOE OSTI2024

Effectiveness and Long-Term Effects of SER+ FELIZ(mente): A School-Based Mindfulness Program for Portuguese Elementary Students

School-based mindfulness programs (SBMPs) have gained global popularity. Yet, there is a need for more rigorous procedures to develop and assess them. This study aimed to address these limitations by examining the effectiveness of a Portuguese SBMP, called SER + FELIZ(mente). The final sample included 190 third and fourth-graders: 99 in the SBMP group and 91 in a wait-list group. Effects on attentional control, emotional regulation and wellbeing were examined in the short term (T2) and 6 months later (T3). We also tested the moderating role of age, gender, and baseline scores. Using a multilevel modeling approach, results showed a clear benefit of SBMP on emotional wellbeing at T2 but not T3. Despite SBMP students surpassing wait-list students in attentional control, cognitive reappraisal, and social wellbeing at T2, these effects were due to a levelling off among SBMP students, coupled with a decline among wait-list students from T1 to T2. These effects were moderated by age at T3. While universal improvements were limited, our SBMP seemed to have acted as a shield against school stressors (i.e., likely, test anxiety at T2). This opens a new avenue for research concerning the role of SBMPs in preventing (rather than repairing) mental-health issues in elementary students.

Magalhães, Sofia↗
DOE OSTI2025

ICARUS at the Short-Baseline Neutrino Program: First Results

The ICARUS collaboration employed the 760-ton T600 detector in a successful three-year physics run at the underground LNGS laboratory. In 2021, ICARUS started its new operation at Fermilab, collecting a substantial amount of neutrino events from the Booster Neutrino Beam (BNB) and the neutrinos at the Main Injector (NuMI) beam off-axis. These were used to test the ICARUS event selection, reconstruction, and analysis algorithms. ICARUS successfully completed its commissioning phase in June 2022, moving then to data taking for neutrino oscillation physics, aiming at first to either confirm or refute the claim by the Neutrino-4 short-baseline reactor experiment. ICARUS will also perform measurements of neutrino cross sections in LAr with the NuMI beam and several Beyond Standard Model studies. After the first year of operations, ICARUS will search for evidence of sterile neutrinos jointly with the Short-Baseline Near Detector, within the Short-Baseline Neutrino program. In this work, preliminary results from the ICARUS data with the BNB and NuMI beams are presented, both in terms of the performance of all ICARUS subsystems and the capability to select and reconstruct neutrino events.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Unlocking Manufacturing Sustainability: Energy Efficiency Opportunities through the US Department of Energy’s Better Plants Program Energy Treasure Hunts (2023–2024)

The US manufacturing sector faces critical challenges: improving sustainability, reducing energy consumption, and reducing greenhouse gas emissions. Energy Treasure Hunt (ETH) training, a service provided by the US Department of Energy’s Better Plants program, offers a compelling solution. Although ETHs have traditionally focused on energy and cost savings, data indicate that ETHs can be used to identify opportunities to reduce emissions and water use and to support a sustainable and circular operation. These 3-day on-site events engage employees in a collaborative search for operational and maintenance efficiency improvement opportunities. The success of ETHs lies in a comprehensive methodology. Each phase in an ETH uses various tools and resources to empower employees to identify practical solutions. This study presents data from 13 ETHs conducted between 2023 and 2024 across diverse manufacturing subsectors in the United States and demonstrates that the events can help create a pragmatic decarbonization pathway. Through the events, a total of 234 energy and emissions reduction opportunities were identified, and the potential impact is significant. Implementing the recommendations could translate to annual savings of 497,299 MMBtu of energy, 64,374 kgal of water, and 4.85 million tCO 2 e of emissions. The fiscal savings from the proposed recommendations translate into nearly $\$$5 million annually. This study identifies the opportunities by energy system type and by the specific actions recommended, while also analyzing the identified opportunities, presenting the most established sustainability recommendations. Case studies from participating partners are presented to further demonstrate that ETHs provide a practical and impactful approach to reducing energy consumption, emissions, and operating costs and promote a more sustainable future for the industrial sector.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

The Backup Program of the Dark Energy Spectroscopic Instrument’s Milky Way Survey

The Milky Way Backup Program (MWBP), a survey currently underway with the Dark Energy Spectroscopic Instrument (DESI) on the Nicholas U. Mayall 4 m Telescope, works at the margins of the DESI Main surveys to obtain spectra of millions of additional stars from the Gaia catalog. Efficiently utilizing times between ∼12° and 18° twilight and poor weather conditions, the MWBP extends the range of stellar sources studied to both brighter magnitudes and lower Galactic latitude and declination than the stars studied in DESI’s Main Milky Way Survey. While the MWBP prioritizes candidate giant stars selected from the Gaia catalog (using color and parallax criteria), it also includes an unbiased sample of bright stars (i.e., 11.2 ≲ G < 16 mag) as well as fainter sources (to G ≲ 19 mag). As of 2025 March 1, the survey had obtained spectra of ∼7 million stars, approximately 1.2 million of which are included in the DESI Data Release 1. The DESI spectra cover the wavelength range from 3600 to 9800 Å at a resolution λ/Δλ varying from 2000 to 5000. The full survey, when completed, will cover an area of more than 21,000 deg 2 and include approximately 10 million Gaia sources, roughly equal to the number of stellar spectra obtained through the DESI Main Survey, while only utilizing ≈9% of all DESI observing time. This paper provides an overview of the MWBP, describing the target selection, observing strategy, and an introduction to the resulting data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The Dark Energy Survey Supernova Program: Cosmological Analysis and Systematic Uncertainties

We present the full Hubble diagram of photometrically classified Type Ia supernovae (SNe Ia) from the Dark Energy Survey supernova program (DES-SN). DES-SN discovered more than 20,000 SN candidates and obtained spectroscopic redshifts of 7000 host galaxies. Based on the light-curve quality, we select 1635 photometrically identified SNe Ia with spectroscopic redshift 0.10 < z < 1.13, which is the largest sample of supernovae from any single survey and increases the number of known z > 0.5 supernovae by a factor of 5. In a companion paper, we present cosmological results of the DES-SN sample combined with 194 spectroscopically classified SNe Ia at low redshift as an anchor for cosmological fits. Here we present extensive modeling of this combined sample and validate the entire analysis pipeline used to derive distances. We show that the statistical and systematic uncertainties on cosmological parameters are ${\sigma }_{{{\rm{\Omega }}}_{M},\mathrm{stat}+\mathrm{sys}}^{{\rm{\Lambda }}\mathrm{CDM}}=$ 0.017 in a flat ΛCDM model, and $({\sigma }_{{{\rm{\Omega }}}_{M}},{\sigma }_{w}{)}_{\mathrm{stat}+\mathrm{sys}}^{w\mathrm{CDM}}$ = (0.082, 0.152) in a flat wCDM model. Combining the DES SN data with the highly complementary cosmic microwave background measurements by Planck Collaboration reduces by a factor of 4 uncertainties on cosmological parameters. In all cases, statistical uncertainties dominate over systematics. We show that uncertainties due to photometric classification make up less than 10% of the total systematic uncertainty budget. This result sets the stage for the next generation of SN cosmology surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The Dark Energy Survey Supernova Program: Light Curves and 5 Yr Data Release

We present griz photometric light curves for the full 5 yr of the Dark Energy Survey Supernova (DES-SN) program, obtained with both forced point-spread function photometry on difference images (DiffImg) performed during survey operations, and scene modelling photometry (SMP) on search images processed after the survey. This release contains 31,636 DiffImg and 19,706 high-quality SMP light curves, the latter of which contain 1635 photometrically classified SNe that pass cosmology quality cuts. This sample spans the largest redshift (z) range ever covered by a single SN survey (0.1 < z < 1.13) and is the largest single sample from a single instrument of SNe ever used for cosmological constraints. We describe in detail the improvements made to obtain the final DES-SN photometry and provide a comparison to what was used in the 3 yr DES-SN spectroscopically confirmed Type Ia SN sample. We also include a comparative analysis of the performance of the SMP photometry with respect to the real-time DiffImg forced photometry and find that SMP photometry is more precise, more accurate, and less sensitive to the host-galaxy surface brightness anomaly. The public release of the light curves and ancillary data can be found at github.com/des-science/DES-SN5YR and doi:10.5281/zenodo.12720777.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

The Dark Energy Bedrock All-sky Supernova Program: Cross Calibration, Simulations, and Cosmology Forecasts

Type Ia supernovae (SNe Ia) have been essential for probing the nature of dark energy; however, most SN analyses rely on the same low-redshift sample, which may lead to shared systematics. In a companion paper, we introduce the Dark Energy Bedrock All-Sky Supernova (DEBASS) program, which has already collected more than 500 low-redshift SNe Ia on the Dark Energy Camera, and present an initial release of 77 SNe Ia within the Dark Energy Survey (DES) footprint observed between 2021 and 2024. Here, we examine the systematics, including photometric calibration and selection effects. We find agreement at the 10 mmag level among the tertiary standard stars of DEBASS, DES, and Pan-STARRS1. Our simulations reproduce the observed distributions of DEBASS SN light-curve properties, and we measure a bias-corrected Hubble residual scatter of 0.08 mag, which, while small, is found in 10% of our simulations. We compare the DEBASS SN distances to the Foundation sample and find consistency with a median residual offset of 0.016 ± 0.019 mag. Selection effects have negligible impacts on distances, but a different photometric calibration solution shifts the median residual −0.015 ± 0.019 mag, highlighting calibration sensitivity. Using conservative simulations, we forecast that replacing historical low-redshift samples with the full DEBASS sample will improve the statistical uncertainties on dark energy parameters w 0 and w a by 30% and 24%, respectively, enhance the dark energy Figure of Merit by up to 60%, and enable a measurement of fσ 8 at the 25% level.

Acevedo, Maria [Duke Univ., Durham, NC (United Sta↗
DOE OSTI2024

Implementing Ordinary Differential Equation Solvers in Rust Programming Language for Modeling Vehicle Powertrain Systems: Preprint

Efficient and accurate ordinary differential equation (ODE) solvers are necessary for powertrain and vehicle dynamics modeling. However, current commercial ODE solvers can be financially prohibitive, leading to a need for accessible, effective, open-source ODE solvers designed for powertrain modeling. Rust is a compiled programming language that has the potential to be used for fast and easy-to-use powertrain models, given its exceptional computational performance, robust package ecosystem, and short time required for modelers to become proficient. However, of the three commonly used (>3,000 downloads) packages in Rust with ODE solver capabilities, only one has more than four numerical methods implemented, and none are designed specifically for modeling physical systems. Therefore, the goal of the Differential Equation System Solver (DESS) was to implement accurate ODE solvers in Rust designed for the component-based problems often seen in powertrain modeling. DESS is a text-based software package that provides a flexible framework for building and solving systems of ODEs. This allows DESS to be included as a dependency for automotive powertrain models that require a variety of solvers and solver configurations. Seven explicit ODE solver methods have been implemented in DESS: Euler’s, Heun’s, midpoint, Ralston’s, classic Runge-Kutta, Bogacki-Shampine, and Cash-Karp. These represent five fixed-step methods and two adaptive-step methods. This paper shows that the solver implementations increase accuracy and computational efficiency compared to Euler's method when modeling a system of three thermal masses in Rust. DESS also includes features designed for modeling component-based physical systems. Users can define relationships between nodes in their system, which the package then translates into a system of equations, leading to simpler and more intuitive code. In the case of a three-thermal-mass system, the user can specify node thermal properties (e.g., thermal capacitance), how nodes are interconnected, and thermal conductance between nodes rather than providing a system of equations. The core contribution from this work is an open-source, text-based Rust package with ODE solvers for automotive powertrain modeling to support cost-free, fast, and accurate simulation.

ADVANCED PROPULSION SYSTEMS↗