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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Implementation of magnetic compressional effects at arbitrary wavelength in the global version of GENE
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Optimizing district energy systems under uncertainty: Insights from a case study from Washington D.C., USA
This study investigates solutions for delivering affordable heating and cooling to a brownfield site, focusing on a case study in Washington, DC. Moving towards more diverse and resilient energy systems, we identify the optimal portfolio for a district energy system with diverse energy sources to meet the area’s energy demands. Our methodological approach integrates two detailed models: one calculating building-level energy demand and the other optimizing district energy technology choices based on their demand profiles, accounting for uncertainties in energy prices, policies, and other parameters. The results provide an economic comparison of district and individual supply options at the building level, emphasizing the flexibility district systems can offer to the electricity sector. District energy systems demonstrate cost-stabilization benefits amidst volatile energy prices and external uncertainties. For heating, district systems yield significant cost savings compared to individual solutions, driven by fuel flexibility and the use of local renewable energy sources. For cooling, district systems also show advantages, though individual systems may remain more cost-effective for smaller buildings. Additionally, district systems exhibit considerable flexibility on the heating side, as evidenced by variations in electricity consumption. We recommend future research to explore the relationship between the economics of district energy systems, particularly at the building level, and their flexibility potential for the electricity sector across diverse geographic contexts to reduce overall grid costs and promote grid reliability. This includes areas with distinct zoning laws, municipal priorities, utility structures, and funding mechanisms, such as the United States, and regions like Europe with pronounced electricity price volatility.
Web-based wide-area monitoring platform for ringdown and clustering analytics in power systems
This paper introduces an open-source research platform for monitoring the Mexican interconnected power grid, allowing real-time processing and information extraction of the grid’s dynamic condition. Moreover, the platform is a Python-based development that embeds different ringdown and clustering analytics tools. In the case of ringdown analysis, the modal information can be extracted using some of the most known algorithms, i.e., Prony analysis, eigensystem realization algorithm (ERA), and matrix pencil (MP). For clustering analysis, the coherent behaviour of generator and non-generator buses is provided by applying recent state-of-the-art techniques such as affinity propagation, K-means, hierarchical agglomerative clustering, and typicality data analysis. The results of up to 93 PMUs show that this open-source platform suits researchers’ and engineers’ power system dynamic analysis requirements.
Benchmarking performance: A round-robin testing for liquid alkaline electrolysis
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Fission gas trapped in Chornobyl fuel microparticles reveals details of reactor operations
The isotopic ratios of fission gas would provide important source information of a nuclear fuel sample found in the environment. However, it is believed that during a reactor accident like Chornobyl all fission gas is lost and that the radioactive particles found in the Chornobyl Exclusion Zone today are depleted in gases by the initial explosion and subsequent fire. We disprove this hypothesis by detection and analysis of trapped krypton and xenon in these particles. Our analysis of krypton and xenon isotopes by noble gas mass spectroscopy in combination with resonance ionization mass spectrometry establishes that important information about reactor operations like age, neutron flux and plutonium fission fraction can still be reconstructed from individual micrometer-sized particles even after decades of weathering in the environment.
Dose and compositional dependence of irradiation-induced property change in FeCr
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Microstructural and material property changes in severely deformed Eurofer-97
Severe plastic deformation changes the microstructure and properties of steels, which may be favourable for their use in structural components of nuclear reactors. In this study, high-pressure torsion (HPT) was used to refine the grain structure of Eurofer-97, a ferritic/martensitic steel. Electron microscopy and X-ray diffraction were used to characterise the microstructural changes. Following HPT at room temperature to a maximum shear strain of 230, the average grain size reduced by a factor of ~30, with a marked increase in high-angle grain boundaries. Dislocation density also increased by more than one order of magnitude. The thermal stability of the deformed material was investigated via in-situ annealing during synchrotron X-ray diffraction. This revealed substantial recovery between 450 K – 800 K. Irradiation with 20 MeV Fe-ions to ~0.1 dpa caused a 20% reduction in dislocation density compared to the as-deformed material. However, HPT deformation prior to irradiation only had a minor effect in mitigating the irradiation-induced reductions in thermal diffusivity and surface acoustic wave velocity of the material. Microstructural and material property changes are dominated by deformation compared to irradiation. In light of this, the benefits of using HPT to improve the irradiation resistance of Eurofer-97 are limited. These results provide a multi-faceted view of the changes in ferritic/martensitic steels due to severe plastic deformation, and how these changes can be used to alter material properties.
Deformation induced microstructure of stress relieved Zircaloy-4 cladding
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Property optimized energy absorber for automotive bumpers utilizing multi-material and structural design strategies
This study proposes a novel design for automotive bumper using optimized lattice structures and multi-materials to balance low-speed collision and high-speed pedestrian impact performance. Different blends of 20 % carbon fiber-reinforced acrylonitrile butadiene styrene with thermoplastic polyurethane were used to tailor material properties. The energy absorber features lattice structures with customized mechanical responses, created by varying the incline angle θ from 0 to 180°. We conducted 576 finite element simulations on a half-scale model to optimize energy absorption and stiffness, leading to 66 optimized designs that met both low-speed and high-speed impact criteria. Two sub-scale optimized energy absorbers with different peak forces—both meeting low-speed impact requirements—were 3D printed and validated through drop-weight testing. The one with lower peak stress demonstrated a more compliant response, exhibiting approximately 90 % lower initial peak force and an increase in energy absorption of around 33 % (from 24 J to 32 J). Compared to the baseline triangular lattice, the optimized absorber increased energy absorption by 68 % from (19 J to 32 J) and reduced peak stress by 70 %. It also showed near-complete recovery with minimal fractures, making it suitable for repeated use. This design improves safety while offering a lightweight, durable, and cost-effective bumper system.
Multi-objective automatic discovery of optimized metamaterials for varying velocity impact protection
Mechanical metamaterials have demonstrated exceptional impact performance while remaining lightweight. Impact resistance has traditionally been investigated using quasi-static simulations, often with the assumption that performance will translate to high-velocity impact scenarios. However, critical crash protection parameters—such as peak stress and absorbed energy—are highly sensitive to impact velocity, leading to inconsistent performance under dynamic loading. To address this, we introduce a strain-rate-aware, active deep learning framework that enables multi-objective optimization of impact protection metrics across a wide range of impact velocities. Our framework captures the strain-rate sensitivity of architected lattices by learning to control spatial gradation in cellular metamaterials, resulting in over 200 % enhancement in impact protection relative to state-of-the-art designs such as Voronoi and re-entrant lattices. We demonstrate its practical utility by designing next-generation lattice structures for automotive bumper systems that satisfy multiple, velocity-specific safety criteria—capabilities beyond those of conventional designs. More than just a predictive tool, this framework marks the first step towards enabling adaptable impact-resistant structures across dynamic regimes.
ChemOS 2.0: An orchestration architecture for chemical self-driving laboratories
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Statistical analysis of HAADF-STEM images to determine the surface coverage and distribution of immobilized molecular complexes
The surface immobilization of molecular catalysts is attractive because it combines the benefits of homogeneous and heterogeneous catalysis. However, determining the surface coverage and distribution of a molecular catalyst on a solid support is often challenging, inhibiting our ability to design improved catalytic systems. Here, in this work, we demonstrate that the combination of scanning transmission electron microscopy (STEM) and image analysis of the individual positions of heavy atoms in transition metal complexes via a convolutional neural network (CNN) allows statistically robust determination of the surface coverage and distribution of immobilized molecular catalysts. These observations provide information about how changes in the functionalization conditions, attachment group, and structure of the molecular catalyst affect the surface coverage and distribution, providing insight into the chemical mechanism of surface immobilization. The method could be generally valuable for correlating the surface coverage and distribution to the activity, selectivity, and stability of a catalytic system.
International fuel performance study of fresh fuel experiments for PCMI effects during RIA experiments
This paper presents the results of High-burnup Experiments for Reactivity-initiated Accident (HERA) Modeling & Simulation (M&S) exercise. The HERA project under the Nuclear Energy Agency (NEA) Second Framework for Irradiation Experiments (FIDES-II) program is focused on studying Light Water Reactor (LWR) fuel behavior during Reactivity-Initiated Accident (RIA) conditions. The Part I M&S cases are based on a series of tests in the Transient Reactor Test (TREAT) facility in the United States and the Nuclear Safety Research Reactor (NSRR) in Japan. The purpose of this work is to evaluate the test design to accomplish its goals in establishing clearer understanding of the effects of power pulse width during RIA conditions. Further, the blind predictions using various computational tools have been performed and compared amongst to interpret the behaviors of high burnup fuels during RIA. While many international participants evaluate the thermal–mechanical behavior of fuel rod under different conditions, a considerable scatter of outputs comes out for the cases due to the disparity between codes in predicting mechanical behaviors. In general, however, the results of thermal–mechanical analysis elaborate that nominal design conditions the shorter pulse width tests in NSRR should cause cladding failures while the TREAT tests appear to have more split prediction of failure or not. Furthermore, the sensitivity analysis varying key testing parameters reveals the considerable effect of power pulse width and total energy deposition on prediction of fuel rod failure.
The forward physics facility: Physics opportunities and conceptual design
The Forward Physics Facility (FPF) is a proposed extension of the HL-LHC program designed to exploit the unique scientific opportunities offered by the intense flux of high energy neutrinos, and possibly new particles, in the far-forward direction. Located in a well-shielded cavern 627 m downstream of one of the LHC interaction points, the facility will support a broad and ambitious physics program that significantly expands the discovery potential of the HL-LHC. Equipped with four complementary detectors -- FLArE, FASER$ν$2, FASER2, and FORMOSA -- the FPF will enable breakthrough measurements that will advance our understanding of neutrino physics, quantum chromodynamics, and astroparticle physics, and will search for dark matter and other new particles. With this Letter of Intent, we propose the construction of the FPF cavern and the construction, integration, and installation of its experiments. We summarize the physics case, the facility design, the layout and components of the detectors, as well as the envisioned collaboration structure, cost estimate, and implementation timeline.
Measurement of the polarizations of prompt and non-prompt J/ψ and ψ ( 2S ) mesons produced in pp collisions at s = 13 TeV
The polarizations of prompt and non-prompt Image 2 and ψ ( 2S ) mesons are measured in proton-proton collisions at s = 13 TeV, using data samples collected by the CMS experiment in 2017 and 2018, corresponding to a total integrated luminosity of 103.3 fb − 1 . Based on the analysis of the dimuon decay angular distributions in the helicity frame, the polar anisotropy, λ ϑ , is measured as a function of the transverse momentum, p T , of the charmonium states, in the 25–120 and 20–100 GeV ranges for the Image 2 and ψ ( 2S ) , respectively. The non-prompt polarizations agree with predictions based on the hypothesis that, for p T ≳ 25 GeV , the non-prompt Image 2 and ψ ( 2S ) are predominantly produced in two-body B meson decays. The prompt results clearly exclude strong transverse polarizations, even for p T exceeding 30 times the Image 2 mass, where λ ϑ tends to an asymptotic value around 0.3. Taken together with previous measurements, by CMS and LHCb at s = 7 TeV, the prompt polarizations show a significant variation with p T , at low p T .
Search for the Higgs boson decays to a ρ 0 , ϕ, or K ⁎0 meson and a photon in proton-proton collisions at $\sqrt{s} = 13$ TeV
Three rare decay processes of the Higgs boson to a ρ(770) 0 , Φ(1020), or K ⁎ (892) 0 meson and a photon are searched for using $\sqrt{s} = 13$ TeV proton-proton collision data collected by the CMS experiment at the LHC. Events are selected assuming the mesons decay into a pair of charged pions, a pair of charged kaons, or a charged kaon and pion, respectively. Depending on the Higgs boson production mode, different triggering and reconstruction techniques are adopted. The analyzed data sets correspond to integrated luminosities up to 138 fb -1 , depending on the reconstructed final state. After combining various data sets and categories, no significant excess above the background expectations is observed. Upper limits at 95% confidence level on the Higgs boson branching fractions into ρ(770) 0 $γ$, Φ(1020)$γ$, and K ⁎ (892) 0 are determined to be 3.7 x 10 -4 , 3.0 x 10 -4 , and 3.0 x 10 -4 , respectively. In case of the ρ(770) 0 $γ$ and Φ(1020)$γ$ channels, these are the most stringent experimental limits to date.
Search for rare decays of the Z and Higgs bosons to a J / ψ or ψ(2S) meson and a photon in proton-proton collisions at s = 13 TeV
A search is presented for rare decays of the Z and Higgs bosons to a photon and a J∕ψ or a ψ(2S) meson, with the charmonium state subsequentially decaying to a pair of muons. The data set corresponds to an integrated luminosity of 123 fb −1 of proton-proton collisions at a center-of-mass energy of 13TeV collected with the CMS detector at the LHC. No evidence for branching fractions of these rare decay channels larger than predicted in the standard model is observed. Upper limits at 95% confidence level are set: $\mathcal{B}$(H → J∕ψγ ) < 2.6 × 10 −4 , $\mathcal{B}$(H → ψ(2S)γ ) < 9.9 × 10 −4 , $\mathcal{B}$(Z → J∕ψγ ) < 0.6 × 10 −6 , and $\mathcal{B}$(Z → ψ(2S)γ ) < 1.3 × 10 −6 . The ratio of the Higgs boson coupling modifiers 𝜅 c ∕𝜅 γ is constrained to be in the interval (−157, +199) at 95% confidence level. Assuming 𝜅 γ = 1, this interval becomes (−166, +208).