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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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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

High-dimensional Data-driven Energy optimization for Multi-Modal Transit Agencies (HD-EMMA) (Final Technical Report)

Public bus transit services in the U.S. are responsible for at least 19.7 million metric tons of CO 2 emission annually. Electric vehicles (EVs) can have a much lower environmental impact than comparable internal combustion engine vehicles (ICEVs), especially in urban areas. Unfortunately, EVs are also much more expensive than ICEVs. As a result, many public transit agencies can afford only mixed fleets of transit vehicles, consisting of EVs, hybrids (HEVs), and ICEVs. Transit agencies that operate such mixed fleets of vehicles face a challenging optimization problem: these agencies need to decide which vehicles are assigned to serving which transit trips. Since the advantage of EVs over ICEVs varies depending on the route and time of day (e.g., the benefit of EVs is higher in slower traffic with frequent stops and lower on highways), the assignment can have a significant effect on energy use and, hence, environmental impact. Through this project, we have developed reference data about energy collections and constructed a set of machine learning models that can accurately predict the energy consumption for the whole fleet at the level of each trip. We have used these models to develop a scheduling and assignment strategy that can rotate the different vehicle types across the transit agencies’ routes. The optimization algorithm ensures that the vehicles are matched to trips considering weather patterns, expected congestion, and road gradients to minimize the overall energy usage. We list the key observations from our project for other practitioners below. Details are available in the report, and the list of source code and our publications are included in the appendix. 1. We have demonstrated the feasibility of collecting, merging and analyzing large volumes of high-resolution real-world telemetry data from a mixed vehicle fleet. To mitigate the inherent noise of the recorded GPS points, the team developed an algorithm that filters data and maps the points onto a street. The algorithm considers previous and subsequent location measurements and different characteristics of nearby streets to determine how likely the vehicle travels on them. Then, the team segmented the time series into disjoint contiguous samples based on adjacent road segments and repeated the outlier detection and removal. For each data point, the team added features corresponding to elevation changes within the samples, weather features, such as temperature, and traffic data, such as speed ratio between actual speed and free-flow speed. 2. We have developed two forms of machine learning models that be used to understand and analyze the energy operations of a mixed vehicle transit fleet. The micro prediction model provides estimates of instantaneous energy prediction for all types of buses (diesel, hybrid, and electric). Such a model is important in evaluating the energy impacts of real-time bus operation strategies, but it is challenging due to diversified driving cycles of transit buses. The model can help the drivers understand the impact of their driving behaviors and short-term congestions. The macro prediction models estimate average energy consumption across the whole trip considering the features: distance traveled, various road-type features, elevation change, day of the week, time of day, various weather features (temperature, humidity, etc.), and traffic features (speed ratio and jam factor). 3. We have demonstrated that it is possible to transfer the machine learning models we have developed in this project to other teams and cities by using inductive transfer learning. We also showed that the performance of the macro energy prediction models can be improved using a multi-task learning approach where the learning parameters are shared between the models being developed for different vehicle types. The advantage of this approach is improved learning performance as the models can exploit common spatio-temporal and environmental characteristics. 4. Finally, we have developed trip and vehicle assignment and scheduling algorithms that use the energy prediction models and develop a trip to vehicle type (diesel, electric, hybrid) assignment for the whole operation to reduce overall emissions and cost. We have shown through simulations that the proposed algorithms can save $\$$ 48,910 in energy costs and 175 metric tons of CO 2 emission annually for CARTA.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Efficient Unitary Designs from Random Sums and Permutations

A unitary k-design is an ensemble of unitaries that matches the first k moments of the Haar measure. In this work, we provide two efficient constructions of k-designs on n-qubits using new random matrix theory techniques. Our first construction is based on exponentiating sums of random i.i.d. Hermitian matrices and uses O(k2n2)-many gates. In the spirit of central limit theorems, we show that this random sum approximates the Gaussian Unitary Ensemble (GUE). We then show that the product of just two exponentiated GUE matrices is already approximately Haar random. Our second construction is based on products of exponentiated sums of random permutations and uses Õ(k poly (n)) many gates. The k dependence is optimal (up to polylogarithmic factors) and is inherited from the efficiency of existing k-wise independent permutations. Furthermore, replacing random permutations with quantum-secure pseudorandom permutations (PRPs), we also obtain a pseudorandom unitary (PRU) ensemble that is secure under nonadaptive queries. A central feature of both proofs is a new connection between the polynomial method in quantum query complexity and the large-dimension (N) expansion in random matrix theory. In particular, the first construction uses the polynomial method to control high moments of certain random matrix ensembles without requiring delicate Weingarten calculations. In doing so, we define and solve a moment problem on the unit circle, asking whether a finite number of equally weighted points can reproduce a given set of moments. In our second construction, the key step is to exhibit an orthonormal basis for irreducible representations of the partition algebra that has a low-degree large-N expansion. This allows us to show that the distinguishing probability is a low-degree rational polynomial of the dimension N.

algebra↗
DOE OSTI2023

The Zintl Concept Applied to Intergrowth Structures: Electron‐Hole Matching, Stacking Preferences, and Chemical Pressures in Pd 5 InAs

Abstract Enumerating the potential stacking sequences of layers is a fundamental way to account for the structure diversity of solid state compounds. In many cases, these stacking variations represent polymorphs with only small energetic differences. Here, we examine a compound for which the preferred stacking pattern instead reveals key aspects about its chemical bonding: Pd 5 InAs. Its structure is based on the intergrowth of slabs of the AuCu 3 and PtHg 2 (or alternatively, fluorite) structure types. Two basic stacking arrangements are available to this compound, represented by the Pd 5 TlAs and HoCoGa 5 structure types. DFT total energy calculations reveal that the former outcompetes the latter by a staggering 0.65 eV/formula unit. Through a combination of DFT‐reversed approximation Molecular Orbital (DFT‐raMO) and DFT‐Chemical Pressure (DFT‐CP) analysis we trace this preference to two factors. First, with DFT‐raMO analysis, we derive a Zintl‐like bonding scheme of Pd 5 InAs. This scheme, along with the inspection of selected crystal orbitals, is then connected to preferred stacking through the coordination environments of the Pd atoms at the interface between the Pd−In and Pd−As layers. In the hypothetical HoCoGa 5 ‐type and observed Pd 5 InAs‐type structures, different Pd coordination environments arise at the interfaces. The hypothetical structure features square planar PdIn 2 As 2 units, in each of which the same 4d‐orbital serves in the Pd sublattice's role as both Lewis acid (for interactions with the As) and Lewis base (for interactions with the In). In the observed structure, tetrahedral PdIn 2 As 2 units occur instead, so that these contradictory roles are distributed to separate 4d‐orbitals, leading to more effective bonding. DFT‐CP analysis illustrates that this driving force for the Pd 5 TlAs‐type arrangement is supplemented by a favorable alignment of the packing tensions in the parent structures. Altogether, the resulting picture demonstrates how the reaction of simple intermetallic structures to form intergrowths can be guided by recognizable chemical interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

HLA-Clus: HLA class I clustering based on 3D structure

In a previous paper, we classified populated HLA class I alleles into supertypes and subtypes based on the similarity of 3D landscape of peptide binding grooves, using newly defined structure distance metric and hierarchical clustering approach. Compared to other approaches, our method achieves higher correlation with peptide binding specificity, intra-cluster similarity (cohesion), and robustness. Here we introduce HLA-Clus, a Python package for clustering HLA Class I alleles using the method we developed recently and describe additional features including a new nearest neighbor clustering method that facilitates clustering based on user-defined criteria. The HLA-Clus pipeline includes three stages: First, HLA Class I structural models are coarse grained and transformed into clouds of labeled points. Second, similarities between alleles are determined using a newly defined structure distance metric that accounts for spatial and physicochemical similarities. Finally, alleles are clustered via hierarchical or nearest-neighbor approaches. We also interfaced HLA-Clus with the peptide:HLA affinity predictor MHCnuggets. By using the nearest neighbor clustering method to select optimal allele-specific deep learning models in MHCnuggets, the average accuracy of peptide binding prediction of rare alleles was improved. The HLA-Clus package offers a solution for characterizing the peptide binding specificities of a large number of HLA alleles. This method can be applied in HLA functional studies, such as the development of peptide affinity predictors, disease association studies, and HLA matching for grafting. HLA-Clus is freely available at our GitHub repository (https://github.com/yshen25/HLA-Clus).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Parametric dependencies of resonant layer responses across linear, two-fluid, drift-MHD regimes

Non-axisymmetric magnetic fields arising in a tokamak either by external or internal perturbations can induce complex non-ideal MHD responses in their resonant surfaces while remaining ideally evolved elsewhere. This layer response can be characterized in a linear regime by a single parameter called the inner-layer Delta, which enables outer-layer matching and the prediction of torque balance to non-linear island regimes. Here, we follow strictly one of the most comprehensive analytic treatments including two-fluid and drift MHD effects and keep the fidelity of the formulation by incorporating the numerical method based on the Riccati transformation when quantifying the inner-layer Delta. The proposed scheme reproduces not only the predicted responses in essentially all asymptotic regimes but also with continuous transitions as well as improved accuracies. In particular, the Delta variations across the inertial regimes with viscous or semi-collisional effects have been further resolved, in comparison with additional analytic solutions. The results imply greater shielding of the electromagnetic torque at the layer than what would be expected by earlier work when the viscous or semi-collisional effects can compete against the inertial effects, and also due to the intermediate regulation by kinetic Alfven wave resonances as rotation slows down. These are important features that can alter the nonaxisymmetric plasma responses including the field penetration by external fields or island seeding process in rotating tokamak plasmas.

MHD instability↗
DOE OSTI2022

Parametric dependencies of resonant layer responses across linear, two-fluid, drift-MHD regimes

Non-axisymmetric magnetic fields arising in a tokamak either by external or internal perturbations can induce complex non-ideal MHD responses in their resonant surfaces while remaining ideally evolved elsewhere. This layer response can be characterized in a linear regime by a single parameter called the inner-layer Δ, which enables outer-layer matching and the prediction of torque balance to non-linear island regimes. In this work, we follow strictly one of the most comprehensive analytic treatments including two-fluid and drift MHD effects and keep the fidelity of the formulation by incorporating the numerical method based on the Riccati transformation when quantifying the inner-layer Δ. The proposed scheme reproduces not only the predicted responses in essentially all asymptotic regimes but also with continuous transitions as well as improved accuracies. In particular, the Δ variations across the inertial regimes with viscous or semi-collisional effects have been further resolved, in comparison with additional analytic solutions. The results imply greater shielding of the electromagnetic torque at the layer than what would be expected by earlier work when the viscous or semi-collisional effects can compete against the inertial effects, and also due to the intermediate regulation by kinetic Alfvén wave resonances as rotation slows down. These are important features that can alter the non-axisymmetric plasma responses including the field penetration by external fields or island seeding process in rotating tokamak plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Understanding and Modeling Pooled Rideshare Acceptance: Influential Factors, Preferred User Experiences, and Implications

Ridesharing allows people to share a vehicle with others traveling in the same direction, which can reduce costs and traffic congestion. Pooled rideshare (PR) services, such as UberX Share and Lyft Shared, offer an economical and environmentally friendly alternative by matching passengers traveling similar routes. However, despite these benefits, PR adoption remains low due to concerns about safety, privacy, and convenience. This research explores the factors influencing PR adoption and provides recommendations to improve user acceptance. A nationwide survey of 5,385 participants across the U.S. was conducted to understand why people choose or avoid PR. The study identified five key factors influencing PR consideration: safety, service experience, privacy, traffic/environment, and time/cost. Additional research examined ways to optimize PR experiences by identifying four critical factors: comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. To measure the impact of these factors, a statistical model called the Pooled Rideshare Acceptance Model (PRAM) was developed, providing insights into how each element influences PR adoption. Further analysis using the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMA) revealed how demographic characteristics such as age, gender, income, and past rideshare experience shape PR perceptions. Some key findings from the multigroup analyses showed that younger users valued technological features and environmental benefits, while older users prioritized reliability and service transparency. Additionally, privacy concerns were more significant for female users, while convenience was critical for higher-income groups. These results emphasize that a 'onesize-fits-all' approach to PR service design is not effective, highlighting the need for tailored strategies to address different user segments. Further, workshops were conducted with researchers and students to translate the findings into real-world solutions. These workshops and 3 all the statistical analyses led to the development of 95 actionable recommendations. The recommendations focus on key areas such as safety, service reliability, user education, and accessibility, offering tangible improvements to PR services. The insights from this study provide valuable guidance for policymakers, transportation network companies (TNCs), and researchers aiming to make PR services safer, more accessible, and widely accepted. By addressing user concerns, PR can become a more viable transportation option, supporting sustainable urban mobility and reducing reliance on private vehicles. Additionally, these findings emphasize the importance of user-centric service design in encouraging broader PR adoption. Future research should explore evolving trends in PR preferences, technological advancements, and policy changes to ensure continued improvements. By implementing these recommendations, PR services can better align with user expectations, enhance trust in shared mobility, and contribute to a more efficient transportation ecosystem.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Optimal white-noise stochastic forcing for linear models of turbulent channel flow

In the present study an optimisation problem is formulated to determine the forcing of an eddy-viscosity-based linearised Navier–Stokes model in channel flow at $Re_\tau \approx 5200$ ( $Re_\tau$ is the friction Reynolds number), where the forcing is white-in-time and spatially decorrelated. The objective functional is prescribed such that the forcing drives a response to best match a set of velocity spectra from direct numerical simulation (DNS), as well as remaining sufficiently smooth. Strong quantitative agreement is obtained between the velocity spectra from the linear model with optimal forcing and from DNS, but only qualitative agreement between the Reynolds shear stress co-spectra from the model and DNS. The forcing spectra exhibit a level of self-similarity, associated with the primary peak in the velocity spectra, but they also reveal a non-negligible amount of energy spent in phenomenologically mimicking the non-self-similar part of the velocity spectra associated with energy cascade. By exploiting linearity, the effect of the individual forcing components is assessed and the contributions from the Orr mechanism and the lift-up effect are also identified. Finally, the effect of the strength of the eddy viscosity on the optimisation performance is investigated. The inclusion of the eddy viscosity diffusion operator is shown to be essential in modelling of the near-wall features, while still allowing the forcing of the self-similar primary peak. In particular, reducing the strength of the eddy viscosity results in a considerable increase in the near-wall forcing of wall-parallel components.

Mechanics↗
DOE OSTI2022

Reactor physics benchmark of Westinghouse PWR core design suite for high burnup/high enrichment fuel - Part 2: 2D and 3D core simulations

Westinghouse has performed a comprehensive set of code-to-code benchmark comparisons to corroborate application of its state-of-the-art NEXUS-based code suite to high burnup and high enrichment (HB/HE) fueled PWR core design and reload analysis. The benchmark consisted of a progression in complexity from modeling single 2D pin cells, through 2D lattices and finally to whole core 2D and then 3D scenarios. The accompanying paper (Part 1) presented the results for a variety of pin cell and lattices representative of high energy cores and established the ability of the MPACT code to accurately match the predictions of the Monte-Carlo code SERPENT2 such that the former may be used as a surrogate reference code in spatially complex whole core modeling applications where using Monte-Carlo methods is somewhat impractical. This paper focuses on comparisons at the whole core level using a representative high power density 3-loop Westinghouse NSSS core operating for 24 month cycles employing high enriched fuel with use of combined burnable absorbers to hold down excess reactivity. The evolution of the core reactivity and power distribution characteristics throughout the cycle are calculated using the Westinghouse NEXUS-based code suite and compared to the results using MPACT. The results of this whole core benchmark confirm the conclusions that the Westinghouse code suite accurately models HB/HE fuel designs, with very good prediction of core reactivity and power distribution throughout the cycle. The industry can be confident that the NEXUS code system can be deployed to support PWR reload safety and operational analyses for cores featuring HB/HE fuel and 24-month cycle operation. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Distinctive features of structural evolution and thermodynamic response in wide-bandgap semiconductors driven by intense electronic excitation

Radiation-tolerant material selection requires balancing lattice rigidity, defect dynamics, and electronic stability, as shown by covalent SiC outperforming ionic Ga 2 O 3 and GaN under extreme environments. Responding to intense electronic excitation, irradiation-driven phase segregation (β → δ/κ in Ga 2 O 3 ) and core–shell track (disordered structure in GaN), accompanied by elemental redistribution, contrastingly, exceptional radiation tolerance manifested by comparatively minimal lattice distortion (0.17 % strain variation) was demonstrated in SiC. These differential responses are primarily attributed to two fundamental mechanisms: (i) thermodynamic driving forces governing defect migration and phase separation, and (ii) the synergistic effects of robust covalent bonding composition coupled with efficient defect recombination processes. Here, the stronger electron–phonon (e-ph) coupling in Ga 2 O 3 (4.34 × 1018 W m −3 K −1 ) and GaN (3.55 × 10 18 W m −3 K −1 ) enhances lattice energy deposition, triggering thermal spikes (ΔT ≫ T m ) and structural transition behaviors, whereas weaker e-ph coupling in SiC (3.69 × 10 18 W m −3 K −1 ), relatively high thermodynamic parameters and efficient energy dissipation suppress thermal spikes to maintaining lattice integrity. The photoresponse degradation driven by enhanced radiative recombination is dominant in N-doped SiC, while V-doped systems achieve defect-mediated photoconduction optimization characterized by abrupt current transitions, matching fluorescence yield evolutions, and directly connecting defect engineering to optoelectronic performance.

Intense electronic excitation↗
DOE OSTI2020

Modeling of fluid injection-induced fault reactivation using coupled fluid flow and mechanical interface model

The present study is aimed at developing a numerical model to reproduce coupled hydro-mechanical processes associated with fault reactivation by fluid injection in low permeability rock, as part of the DECOVALEX-2019 project Task B. We proposed a modeling approach for simulating the processes using the TOUGH-FLAC simulator, and modeled a fault reactivation experiment conducted at Mont Terri Rock Laboratory in Switzerland. The first step of the study involved benchmark calculations considering a simplified fault plane and geometry. Fluid flow along a fault was modeled using elements of aperture-sized thickness on the basis of Darcy's law and the cubic law in TOUGH2, whereas the mechanical behavior of a single fault was represented by zero-thickness interface elements in FLAC3D upon which a slip and/or separation is allowed. A methodology to connect a TOUGH2 volume element to a FLAC3D interface element was developed for handling the hydro-mechanical interactions on the fault during fluid injection. Two different fault models for describing the evolutions of hydraulic aperture by elastic fracture opening and failure-induced aperture increase were considered in the benchmark calculations. In the coupling process, the changes in geometrical features and hydrological properties induced by mechanical deformation were continuously updated. The transient responses of the fault and host rock to stepwise pressurization were examined during the simulation. The hydro-mechanical behavior, including the injection flow rate, pressure distribution around the borehole, stress conditions, and displacements in normal and shear directions were monitored in the surrounding rock and along the fault. The results of benchmark calculations suggest that the developed model reasonably represents the hydro-mechanical behavior of a fault and the surrounding rock. This modeling approach was applied to the fault reactivation experiment of the Mont Terri Rock Laboratory. In this interpretive modeling, a parametric study was conducted to examine the effects of input parameters regarding in situ stress and fault properties on the hydro-mechanical responses of the fault to water injection. Then, an optimal parameter set to reproduce the field experiment results was chosen by trial-and-error. The injection flow rate and pressure response during fault reactivation closely matched those obtained at the site, which indicates the capability of the model to appropriately capture the progressive pathway evolution during fault reactivation tests at the site. Finally, the anchor displacements were overestimated by the model, but a fair agreement was obtained in terms of the order of magnitude and the variation tendency.

58 GEOSCIENCES↗
DOE OSTI2022

The MeerKAT Galaxy Cluster Legacy Survey I. Survey Overview and Highlights

MeerKAT’s large number (64) of 13.5 m diameter antennas, spanning 8 km with a densely packed 1 km core, create a powerful instrument for wide-area surveys, with high sensitivity over a wide range of angular scales. The MeerKAT Galaxy Cluster Legacy Survey (MGCLS) is a programme of long-track MeerKAT L-band (900–1670 MHz) observations of 115 galaxy clusters, observed for ~6–10 h each in full polarisation. The first legacy product data release (DR1), made available with this paper, includes the MeerKAT visibilities, basic image cubes at ~8" resolution, and enhanced spectral and polarisation image cubes at ~8" and 15" resolutions. Typical sensitivities for the full-resolution MGCLS image products range from ~3–5 μJy beam –1 . The basic cubes are full-field and span 2° × 2°. The enhanced products consist of the inner 1.2° × 1.2° field of view, corrected for the primary beam. The survey is fully sensitive to structures up to ~10' scales, and the wide bandwidth allows spectral and Faraday rotation mapping. Relatively narrow frequency channels (209 kHz) are also used to provide H I mapping in windows of 0 < z < 0.09 and 0.19 < z < 0.48. Here, in this paper, we provide an overview of the survey and the DR1 products, including caveats for usage. We present some initial results from the survey, both for their intrinsic scientific value and to highlight the capabilities for further exploration with these data. These include a primary-beam-corrected compact source catalogue of ~626 000 sources for the full survey and an optical and infrared cross-matched catalogue for compact sources in the primary-beam-corrected areas of Abell 209 and Abell S295. We examine dust unbiased star-formation rates as a function of cluster-centric radius in Abell 209, extending out to 3.5 R 200 . We find no dependence of the star-formation rate on distance from the cluster centre, and we observe a small excess of the radio-to-100 μm flux ratio towards the centre of Abell 209 that may reflect a ram pressure enhancement in the denser environment. We detect diffuse cluster radio emission in 62 of the surveyed systems and present a catalogue of the 99 diffuse cluster emission structures, of which 56 are new. These include mini-halos, halos, relics, and other diffuse structures for which no suitable characterisation currently exists. We highlight some of the radio galaxies that challenge current paradigms, such as trident-shaped structures, jets that remain well collimated far beyond their bending radius, and filamentary features linked to radio galaxies that likely illuminate magnetic flux tubes in the intracluster medium. We also present early results from the H I analysis of four clusters, which show a wide variety of H I mass distributions that reflect both sensitivity and intrinsic cluster effects, and the serendipitous discovery of a group in the foreground of Abell 3365.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Merger identification through photometric bands, colours, and their errors

Aims. We present the application of a fully connected neural network (NN) for galaxy merger identification using exclusively photometric information. Our purpose is not only to test the method’s efficiency, but also to understand what merger properties the NN can learn and what their physical interpretation is. Methods. We created a class-balanced training dataset of 5860 galaxies split into mergers and non-mergers. The galaxy observations came from SDSS DR6 and were visually identified in Galaxy Zoo. The 2930 mergers were selected from known SDSS mergers and the respective non-mergers were the closest match in both redshift and r magnitude. The NN architecture was built by testing a different number of layers with different sizes and variations of the dropout rate. We compared input spaces constructed using: the five SDSS filters: u, g, r, i, and z; combinations of bands, colours, and their errors; six magnitude types; and variations of input normalization. Results. We find that the fibre magnitude errors contribute the most to the training accuracy. Studying the parameters from which they are calculated, we show that the input space built from the sky error background in the five SDSS bands alone leads to 92.64 ± 0.15% training accuracy. We also find that the input normalization, that is to say, how the data are presented to the NN, has a significant effect on the training performance. Conclusions. We conclude that, from all the SDSS photometric information, the sky error background is the most sensitive to merging processes. This finding is supported by an analysis of its five-band feature space by means of data visualization. Moreover, studying the plane of the g and r sky error bands shows that a decision boundary line is enough to achieve an accuracy of 91.59%.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Molten Salt Reactor Experiment Benchmark Evaluation

As one of the six advanced reactor concepts selected by the 2002 Generation IV roadmap as a technology meriting future research, Molten Salt Reactors (MSRs) attracted broad attention and multiple private and public entities are working towards its commercialization. Facing stringent regulatory requirements, validations of computational codes used to calculate and prove the safety characteristics of MSRs play a key role. This project developed the world-first, MSR-related reactor physics benchmark based on a series of zero-power experiments conducted at the Molten Salt Reactor Experiment (MSRE). The benchmark was successfully reviewed by the International Reactor Physics Experiment Evaluation Project (IRPhEP) committee and is available in the IRPhEP handbook starting form the 2019 edition. To benchmark experimental data from the MSRE, a three-dimensional high-fidelity model was developed using the Monte Carlo neutron transport code Serpent 2 and ad-hoc methods have been implemented to account for the unique feature of fuel salt motion. The calculated effective multiplication factor, k eff , for the first criticality experiment, when 235 U was progressively added to the fuel salt in order to achieve criticality with stationary salt and isothermal conditions, was 1.02132 (±3 pcm). The total uncertainty for experimental k eff was estimated to be 420 pcm. The calculated k eff is 2.154% larger than the experimental and benchmark model value, which is approximately five times the benchmark model uncertainty. It is to be noted that, for systems containing large volume of graphite (or other carbonaceous materials), Monte Carlo codes tend to overestimate the keff of the benchmark model by 1% to 2%. The bias is possibly due to uncertainties in the impurity content of the graphite blocks, uncertainty in the neutron capture cross section of carbon or uncertainty in the properties of nuclear-grade graphite. The calculated reactivity coefficient of 235 U concentration (0.2228 ± 0.0014, represented as the change of reactivity over the relative change of 235 U mass in loop) matches well with the experimental value (0.223 ± 0.006), strengthening the confidence on the accurate representation of the fuel salt composition in the MSRE benchmark. Additional reactivity effect calculations, including the control rod bank worth, reactivity effects of fuel circulation and isothermal and fuel temperature coefficients show good agreement with the experimental values (within 1σ) as well.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Star formation characteristics of CNN-identified post-mergers in the Ultraviolet Near Infrared Optical Northern Survey (UNIONS)

ABSTRACT The importance of the post-merger epoch in galaxy evolution has been well documented, but post-mergers are notoriously difficult to identify. While the features induced by mergers can sometimes be distinctive, they are frequently missed by visual inspection. In addition, visual classification efforts are highly inefficient because of the inherent rarity of post-mergers (~1 per cent in the low-redshift Universe), and non-parametric statistical merger selection methods do not account for the diversity of post-mergers or the environments in which they appear. To address these issues, we deploy a convolutional neural network (CNN) that has been trained and evaluated on realistic mock observations of simulated galaxies from the IllustrisTNG simulations, to galaxy images from the Canada France Imaging Survey, which is part of the Ultraviolet Near Infrared Optical Northern Survey. We present the characteristics of the galaxies with the highest CNN-predicted post-merger certainties, as well as a visually confirmed subset of 699 post-mergers. We find that post-mergers with high CNN merger probabilities [p(x) > 0.8] have an average star formation rate that is 0.1 dex higher than a mass- and redshift-matched control sample. The SFR enhancement is even greater in the visually confirmed post-merger sample, a factor of 2 higher than the control sample.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

An Experimental Investigation of Heat Transfer for Supercritical Carbon Dioxide Cooling in a Staggered Pin Fin Array

Pin fin geometry in the trailing edge as turbulators in tur-bine blades has been a subject thoroughly researched for the past several decades. To improve the efficiency of turbomachinery, higher turbine inlet temperatures are necessary, introducing the need for internal cooling channel features, such as pin fin arrays. These solutions are necessary to avoid material failure at higher operating conditions by enhancing the heat transfer of the blade. Over the past decade, a shift toward applying supercritical carbon dioxide or sCO2 cycles has shown increased potential compared to the industry standard air cycles. Promising higher cycle ef¬ficiency, smaller footprints, and zero greenhouse gas emissions, sCO2 has become an attractive alternative to well-established air cycles. This alternative cycle introduces the need to re-visit the heat transfer capabilities of internal cooling geometries within this novel operating fluid environment. The first objective of this paper is to experimentally demonstrate the heat transfer charac-teristics of sCO2 as the secondary internal cooling fluid environ¬ment through testing a pin fin array in a closed loop at 200 bar and 400 Celsius inlet conditions, which stands well within the supercritical region of sCO2. Secondly, the experimental process aims to validate numerical simulation methodology for studying sCO2 environments for internal cooling of turbine blades in a matching pin fin geometry.

Wardell, Ryan J↗
DOE OSTI2026

Data & Code from Phoenix CPPP Phase 2 Analysis

This data and code package supports the analysis presented in “Beyond Surface Cooling: Comprehensive Field Assessment of Reflective Pavement Thermal Performance in Phoenix, Arizona” and provides fully reproducible workflows for evaluating the thermal performance of cool pavement treatments in a hot urban environment. The dataset integrates multi-modal field measurements collected across residential and nonresidential settings, including mobile air temperature traverses, stationary air temperature monitoring, residential mean radiant temperature (MRT) measurements, subsurface temperature profiles, and controlled testbed observations. The data package contains raw and processed datasets in comma-separated value (CSV) format, accompanying metadata files describing site characteristics and measurement protocols, and R scripts (.R files) used for data cleaning, time synchronization, spatial and temporal matching, quality control filtering, statistical comparison, and figure generation. All analyses were conducted using R (version ≥ 4.2.0) with commonly available packages (e.g., tidyverse, lubridate, data.table, ggplot2). No proprietary software is required to reproduce results. Field campaigns were designed to quantify the effects of high-reflectance pavement coatings on surface temperature, near-surface air temperature, subsurface heat propagation, and radiative heat exposure. Temporal alignment procedures include standardized timestamp conversion and nearest-neighbor matching of high-frequency sensor measurements to stop-based metadata within defined tolerance windows to ensure comparability across instruments. The workflows generate summary statistics, treatment–control contrasts, depth-dependent thermal gradients, and time-series visualizations used in the associated publication. By integrating mobile, stationary, radiative, and subsurface measurements within a unified and transparent processing framework, this package enables comprehensive evaluation of cool pavement performance across multiple thermal exposure pathways and supports reuse in future urban heat mitigation and climate resilience studies.

AIR TEMPERATURE↗
DOE OSTI2021

The Atacama Cosmology Telescope: A Catalog of >4000 Sunyaev–Zel’dovich Galaxy Clusters

Here, we present a catalog of 4195 optically confirmed Sunyaev–Zel’dovich (SZ) selected galaxy clusters detected with signal-to-noise ratio >4 in 13,211 deg 2 of sky surveyed by the Atacama Cosmology Telescope (ACT). Cluster candidates were selected by applying a multifrequency matched filter to 98 and 150 GHz maps constructed from ACT observations obtained from 2008 to 2018 and confirmed using deep, wide-area optical surveys. The clusters span the redshift range 0.04 < z < 1.91 (median z = 0.52). The catalog contains 222 z > 1 clusters, and a total of 868 systems are new discoveries. Assuming an SZ signal versus mass-scaling relation calibrated from X-ray observations, the sample has a 90% completeness mass limit of M 500 c > 3.8 × 10 14 M ⊙ , evaluated at z = 0.5, for clusters detected at signal-to-noise ratio >5 in maps filtered at an angular scale of 2.'4. The survey has a large overlap with deep optical weak-lensing surveys that are being used to calibrate the SZ signal mass-scaling relation, such as the Dark Energy Survey (4566 deg 2 ), the Hyper Suprime-Cam Subaru Strategic Program (469 deg 2 ), and the Kilo Degree Survey (825 deg 2 ). We highlight some noteworthy objects in the sample, including potentially projected systems, clusters with strong lensing features, clusters with active central galaxies or star formation, and systems of multiple clusters that may be physically associated. The cluster catalog will be a useful resource for future cosmological analyses and studying the evolution of the intracluster medium and galaxies in massive clusters over the past 10 Gyr.

79 ASTRONOMY AND ASTROPHYSICS↗