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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2010

Scalar dark matter and standard model with four generations

We consider a scalar dark-matter (DM) model, the SM4+D, consisting of the standard model with four generations (SM4) and a real gauge-singlet scalar called darkon, D, as the WIMP DM candidate. We explore constraints on the darkon sector of the SM4+D from WIMP DM direct-search experiments, including CDMS-II and CoGeNT, and from the decay of a B meson into a kaon plus missing energy. We find that a sizable portion of the darkon parameter space is still compatible with the experimental data. Since the darkon-Higgs interaction may give rise to considerable enhancement of the Higgs invisible decay mode, the existence of the darkon could lead to the weakening or evasion of some of the restrictions on the Higgs mass in the presence of fourth-generation quarks. In addition, it can affect the flavor-changing decays of these new heavy quarks into a lighter quark and the Higgs boson, as the Higgs may subsequently decay invisibly. Therefore, we also study these flavor-changing neutral transitions involving the darkon, as well as the corresponding top-quark decay t{yields}cDD, some of which may be observable at the Tevatron or LHC and thus provide additional tests for the SM4+D.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Physics-informed semantic inpainting: Application to geostatistical modeling

A fundamental problem in geostatistical modeling is to infer the heterogeneous geological field based on limited measurements and some prior spatial statistics. Semantic inpainting, a technique for image processing using deep generative models, has been recently applied for this purpose, demonstrating its effectiveness in dealing with complex spatial patterns. However, the original semantic inpainting framework incorporates only information from direct measurements, while in geostatistics indirect measurements are often plentiful. In this work, to overcome this limitation, we propose a physics-informed semantic inpainting framework, employing the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and jointly incorporating the direct and indirect measurements by exploiting the underlying physical laws. Our simulation results for a high-dimensional problem with 512 dimensions show that in the new method, the physical conservation laws are satisfied and contribute in enhancing the inpainting performance compared to using only the direct measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

SU-F-T-447: The Impact of Treatment Planning Methods On RapidPlan Modeling for Rectum Cancer

Purpose: To investigate the dose volume histogram (DVH) prediction varieties based on intensity modulate radiotherapy (IMRT) plan or volume arc modulate radiotherapy (VMAT) plan models on the RapidPlan. Methods: Two DVH prediction models were generated in this study, including an IMRT model trained from 83 IMRT rectum plans and a VMAT model trained from 60 VMAT rectum plans. In the internal validation, 20 plans from each training database were selected to verify the clinical feasibility of the model. Then, 10 IMRT plans (PIMRT-by-IMRT-model) generated from IMRT model and 10 IMRT plans generated from VMAT model (PIMRT-by-VMAT-model) were compared on the dose to organs at risk (OAR), which included bladder, left and right femoral heads. The similar comparison was also performed on the VMAT plans generated from IMRT model (PVMAT-by-IMRT-model) and VMAT plans generated from VMAT (PVMAT-by-VMAT-model) model. Results: For the internal validation, all plans from IMRT or VMAT model shows significantly improvement on OAR sparing compared with the corresponded clinical ones. Compared to the PIMRT-by-VMAT-model, the PIMRT-by-IMRT-model has a reduction of 6.90±3.87%(p<0.001) on V40 6.63±3.62%(p<0.001) on V45 and 4.74±2.26%(p<0.001) on V50 in bladder; and a mean dose reduction of 2.12±1.75Gy(p=0.004) and 2.84±1.53Gy(p<0.001) in right and left femoral head, respectively. There was no significant difference on OAR sparing between PVMAT-by-IMRT-model and PVMAT-by-VMAT-model. Conclusion: The IMRT model for the rectal cancer in the RapidPlan can be applied to for VMAT planning. However, the VMAT model is not suggested to use in the IMRT planning. Cautions should be taken that the planning model based on some technique may not feasible to other planning techniques.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Triadic Closure in Configuration Models with Unbounded Degree Fluctuations

The configuration model generates random graphs with any given degree distribution, and thus serves as a null model for scale-free networks with power-law degrees and unbounded degree fluctuations. For this setting, we study the local clustering c(k), i.e., the probability that two neighbors of a degree-k node are neighbors themselves. We show that c(k) progressively falls off with k and the graph size n and eventually for k=Ω(√n) settles on a power law c(k)∼n{sup 5−2τ}k{sup −2(3−τ)} with τ∈(2,3) the power-law exponent of the degree distribution. This fall-off has been observed in the majority of real-world networks and signals the presence of modular or hierarchical structure. Our results agree with recent results for the hidden-variable model and also give the expected number of triangles in the configuration model when counting triangles only once despite the presence of multi-edges. We show that only triangles consisting of triplets with uniquely specified degrees contribute to the triangle counting.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Modeling household-level party composition behavior for multiparty activities: a random parameter nested logit modeling approach

This study presents findings of a household-level party composition model for multiparty activities. It exploits data from a comprehensive Household Travel Survey conducted by Chicago Metropolitan Agency of Planning. The study estimates a random parameter nested logit model to capture households’ unobserved preference heterogeneity and non-proportional substitution patterns in terms of activity party composition for multiparty activities. A wide variety of household demographics, activity attributes and residential neighborhood characteristics are examined in this paper. The magnitude of the impacts of the determinants are tested in this study by analyzing the elasticity of the variables, which suggests that household demographics and attributes of the multiparty activities have significant effects on the household-level activity party composition. Residential neighborhood characteristics, although somewhat less impactful, still play a meaningful role. This model will be implemented within the POLARIS transportation systems simulator to improve the activity generation modeling workflow, and the prediction accuracy of various activity-travel components.

activity party composition↗
DOE OSTI2011

Statistical sum in the CFT driven cosmology

The path integration technique recently developed for the statistical sum of the microcanonical ensemble in cosmology is applied to the calculation of the one-loop preexponential factor in the cosmological model generated by a conformal field theory with a large number of quantum species — the model of initial conditions possibly related to the resolution of the cosmological constant and landscape problems. The result is obtained for the family of background cosmological instantons with one oscillation of the FRW scale factor. The magnitude of the prefactor is analytically and numerically estimated for fields of various spins conformally coupled to gravity, which justifies the validity of semiclassical expansion for this family of cosmological instantons.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

SU-E-T-129: Are Knowledge-Based Planning Dose Estimates Valid for Distensible Organs?

Purpose: Knowledge-based planning programs have become available to assist treatment planning in radiation therapy. Such programs can be used to generate estimated DVHs and planning constraints for organs at risk (OARs), based upon a model generated from previous plans. These estimates are based upon the planning CT scan. However, for distensible OARs like the bladder and rectum, daily variations in volume may make the dose estimates invalid. The purpose of this study is to determine whether knowledge-based DVH dose estimates may be valid for distensible OARs. Methods: The Varian RapidPlan™ knowledge-based planning module was used to generate OAR dose estimates and planning objectives for 10 prostate cases previously planned with VMAT, and final plans were calculated for each. Five weekly setup CBCT scans of each patient were then downloaded and contoured (assuming no change in size and shape of the target volume), and rectum and bladder DVHs were recalculated for each scan. Dose volumes were then compared at 75, 60,and 40 Gy for the bladder and rectum between the planning scan and the CBCTs. Results: Plan doses and estimates matched well at all dose points., Volumes of the rectum and bladder varied widely between planning CT and the CBCTs, ranging from 0.46 to 2.42 for the bladder and 0.71 to 2.18 for the rectum, causing relative dose volumes to vary between planning CT and CBCT, but absolute dose volumes were more consistent. The overall ratio of CBCT/plan dose volumes was 1.02 ±0.27 for rectum and 0.98 ±0.20 for bladder in these patients. Conclusion: Knowledge-based planning dose volume estimates for distensible OARs are still valid, in absolute volume terms, between treatment planning scans and CBCT’s taken during daily treatment. Further analysis of the data is being undertaken to determine how differences depend upon rectum and bladder filling state. This work has been supported by Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Proposal for New Plant Controller and Electrical Controller

This is the first revision of this memo capturing in more detail some items discussed at the last WECC MVS meeting in January, 2022 during the REMWG part of the meeting. The items here are presented for further enhancements in the electrical controls model and plant controller model (specifically for hybrid-plants or plants with multiple aggregated inverter-based generation models). These will need to be discussed and refined, and then implemented for benchmark testing and final approval.

42 ENGINEERING↗
DOE OSTI2025

Implementation of INCL nuclear model in GENIE Generator

The Liège Intranuclear Cascade (INCL) model is a nuclear-physics model that simulates hadron (baryon, anti-baryon and meson) reactions on nuclei, for incident energies ranging from a few tens of MeV to 10-20 GeV. The INCL model has been well validated by hadron scattering data. In my work, I implement an interface in GENIE to use the INCL nuclear model in the simulations of both the initial state of the target nucleus and the Final State Interaction in neutrino-nucleus interaction. It has a consistent treatment of nuclear models in both neutrino interaction and hadron rescattering. A full event record including neutrino vertex and each vertex of hadron rescattering has been accomplished. Several processes, e.g. cluster production, Delta transportation and de-excitation will also be included as benefits of the implementation of the INCL model in GENIE. I will show some initial simulation results showcasing the new GENIE features and discuss plans for making them available for use in experimental analyses.

Liu, Liang [Fermilab] (ORCID:000000026753925X)↗
DOE OSTI2022

Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover

Feedstock heterogeneity is a key challenge impacting the deconstruction and conversion of herbaceous lignocellulosic biomass to biobased fuels, chemicals, and materials. Upstream processing to homogenize biomass feedstock streams into their anatomical components via air classification allows for a more tailored approach to subsequent mechanical and chemical processing. Here, we show that differing corn stover anatomical tissues respond differently to pretreatment and enzymatic hydrolysis and therefore, a one-size-fits-all approach to chemical processing biomass is inappropriate. To inform on-line downstream processing, a robust and high-throughput analytical technique is needed to quantitatively characterize the separated biomass. Predictive correlation of near-infrared spectra to biomass chemical composition is such a technique. Here, we demonstrate the capability of models developed using an “off-the-shelf,” industrially relevant spectrometer with limited spectral range to make strong predictions of both cell wall chemical composition and the relative abundance of anatomical components of the corn stover, the latter for the first time ever. Gaussian process regression (GPR) yields stronger correlations (average R 2 v = 88% for chemical composition and 95% for anatomical relative abundance) than the more commonly used partial least squares (PLS) regression (average R 2 v = 84% for chemical composition and 92% for anatomical relative abundance). In nearly all cases, both GPR and PLS outperform models generated using neural networks. These results highlight the potential for coupling NIRS with predictive models based on GPR due to the potential to yield more robust correlations.

09 BIOMASS FUELS↗
DOE OSTI2019

A Novel Statistical Method for Measuring the Temperature–Density Relation in the IGM Using the b – N Hi Distribution of Absorbers in the Ly α Forest

We propose a new method for determining the thermal state of the intergalactic medium based on Voigt profile decomposition of the Lyα forest. The distribution of Doppler parameter and column density (b–N H i distribution) is sensitive to the temperature–density relation T = T 0 (ρ/ρ 0 ) γ-1 , and previous work has inferred T 0 and γ by fitting its low-b cutoff. This approach discards the majority of available data and is susceptible to systematics related to cutoff determination. We introduce an approach that exploits all information encoded in the b – N H i distribution by modeling its entire shape. We apply kernel density estimation to discrete absorption lines to generate model probability density functions, and then we use principal component decomposition to create an emulator that can be evaluated anywhere in thermal parameter space. We introduce a Bayesian likelihood based on these models enabling parameter inference via Markov Chain Monte Carlo. The method's robustness is tested by applying it to a large grid of thermal history simulations. By conducting 160 mock measurements, we establish that our approach delivers unbiased estimates and valid uncertainties for a 2D ( T 0 , γ) measurement. Furthermore, we conduct a pilot study applying this methodology to real observational data at z = 2. Using 200 absorbers, equivalent in path length to a single Lya forest spectrum, we measure $\mathrm{log}{T}_{0}={4.092}_{-0.055}^{+0.050}$ and $\gamma ={1.49}_{-0.074}^{+0.073}$ in excellent agreement with cutoff fitting determinations using the same data. Our method is far more sensitive than cutoff fitting, enabling measurements of log T 0 and γ with precision on $\mathrm{log}{T}_{0}$ (γ) nearly two (three) times higher for current data set sizes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Debiasing with Diffusion: Probabilistic Reconstruction of Dark Matter Fields from Galaxies with CAMELS

Abstract Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter (DM) components that cannot be directly observed. Galaxy formation simulations can be used to study the relationship between DM density fields and galaxy distributions. However, this relationship can be sensitive to assumptions in cosmology and astrophysical processes embedded in galaxy formation models, which remain uncertain in many aspects. In this work, we develop a diffusion generative model to reconstruct DM fields from galaxies. The diffusion model is trained on the CAMELS simulation suite that contains thousands of state-of-the-art galaxy formation simulations with varying cosmological parameters and subgrid astrophysics. We demonstrate that the diffusion model can predict the unbiased posterior distribution of the underlying DM fields from the given stellar density fields while being able to marginalize over uncertainties in cosmological and astrophysical models. Interestingly, the model generalizes to simulation volumes ≈500 times larger than those it was trained on and across different galaxy formation models. The code for reproducing these results can be found athttps://github.com/victoriaono/variational-diffusion-cdm✎.

Astronomy & Astrophysics↗
DOE OSTI2015

Biomass Scenario Model: BETO Analysis Platform Peer Review; NREL (National Renewable Energy Laboratory)

The Biomass Scenario Model (BSM) is a unique, carefully validated, state-of-the-art fourth-generation model of the domestic bioenergy supply chain which explicitly focuses on policy issues and their potential side effects. It integrates resource availability, behavior, policy, and physical, technological, and economic constraints. The BSM uses system-dynamics simulation to model dynamic interactions across the supply chain; it tracks the deployment of biofuels given technological development and the reaction of the investment community to those technologies in the context of land availability, the competing oil market, consumer demand for biofuels, and government policies over time. It places a strong emphasis on the behavior and decision-making of various economic agents. The model treats the major infrastructure-compatible fuels. Scenario analysis based on the BSM shows that the biofuels industry tends not to rapidly thrive without significant external actions in the early years of its evolution. An initial focus for jumpstarting the industry typically has strongest results in the BSM in areas where effects of intervention have been identified to be multiplicative. In general, we find that policies which are coordinated across the whole supply chain have significant impact in fostering the growth of the biofuels industry and that the production of tens of billions of gallons of biofuels may occur under sufficiently favorable conditions.

09 BIOMASS FUELS↗
DOE OSTI2019

Real-time estimation of solar irradiation

Aspects relate to methods, systems, and computer program products for estimating future levels of solar power received at a sensor including receiving, by a processor, a first measured power value at a first time from the sensor, calculating a first value of a dynamic correction factor based on the first measured power value associated with the first time, applying the first value of the calculated dynamic correction factor to a power estimation model, generating a first power estimation value for a second time using the power estimation model, wherein the second time is later than the first time, receiving a second measured power value at the second time, and calculating a second value of a dynamic correction factor based on the second measured power value associated with the second time and the first value of the dynamic correction factor.

Dang, Hien P.↗
DOE OSTI2025

ERA5-Land Data for LASSO-CACTI Overview Paper

The European Centre for Medium-Range Weather Forecasts (ECMWF) generated a soil reanalysis dataset for the land component of the fifth generation of European ReAnalysis (ERA5), referred to as ERA5-Land. This is a model-generated dataset, with the original version available for the period 1950 to present. The version archived in this DOE ARM product is a subset of the data is for the period of the CACTI field campaign plus several preceding months, specifically from August 1, 2018 through March 22, 2019 with hourly intervals. The ARM copy is also a sub-region of the original global product; the ARM copy is for -60 to -5 °N by -105 to -30 °W. Only variables necessary to drive the WRF-Hydro model are included, which are the 2-m temperature and specific humidity, 10-m wind components, surface pressure, rain rate, and downward surface short and longwave radiation. These data have been obtained from the Copernicus Data Store.

10m wind u-component↗
DOE OSTI2022

Ephemeral Learning - Augmenting Triggers with Online-Trained Normalizing Flows

The large data rates at the LHC require an online trigger system to select relevant collisions. Rather than compressing individual events, we propose to compress an entire data set at once. We use a normalizing flow as a deep generative model to learn the probability density of the data online. The events are then represented by the generative neural network and can be inspected offline for anomalies or used for other analysis purposes. We demonstrate our new approach for a toy model and a correlation-enhanced bump hunt.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Application of the metabolic modeling pipeline in KBase to categorize reactions, predict essential genes, and predict pathways in an isolate genome

The DOE Systems Biology Knowledgebase (KBase) platform offers a range of powerful tools for the reconstruction, refinement, and analysis of genome-scale metabolic models built from microbial isolate genomes. In this chapter, we describe and demonstrate these tools in action with an analysis of isoprene production in the Bacillus subtilis DSM genome. Two different methods are applied to build initial metabolic models for the DSM genome, then the models are gapfilled in three different growth conditions. Next, flux balance analysis (FBA) and flux variability analysis (FVA) techniques are applied to both study the growth of these models in minimal media and classify reactions within each model based on essentiality and functionality. The models are applied with the FBA method to predict essential genes, which are then compared to an updated list of essential genes obtained for B. subtilis 168, a very similar strain to the DSM isolate. The models are also applied to simulate Biolog growth conditions, and these results are compared with Biolog data collected for B. subtilis 168. Finally, the DSM metabolic models are applied to explore the pathways and genes responsible for producing isoprene in this strain. These studies demonstrate the accuracy and utility of models generated from the KBase pipelines, as well as exploring the tools available for analyzing these models.

DOE knowledgebase↗
DOE OSTI2017

A Geostatistical Study in Support of CO 2 Storage in Deep Saline Aquifers of the Shenhua CCS Project, Ordos Basin, China

The Shenhua Carbon Capture and Storage (CCS) project at the Shenbei Slope injection site in North Yulin is the first 100,000 ton-per-year scale CCS pilot project in China with an injection operation lasting nearly 3 years. In this study, we investigate various geostatistical methods and their impact on the respective geologic models on which simulation is performed to understand the phenomena observed during 3 years of Shenhua CCS operations. Although there was a brief period of wellhead pressure increase at the injection well, it unexpectedly dropped for most of the time. Another interesting observation showed that the majority of CO 2 gas injection was received by the topmost sandstone Liujiagou formation instead of the basement limestone Majiagou formation, which was predicted to have much more injectivity and storage capacity. Based on the current geostatistical methods and available data, 3 steps of reservoir modeling and flow simulation are carried out and they go from having homogeneous property models to incorporating standard 2-point geostatistical methods to using object-based models. The layer-cake models generate a rather uniform plume shape and increased pressure response. Meanwhile, two-point statistical models add more complexity to the size and shape of CO 2 plume, however are not capable of reproducing the pressure decline behavior. These results demonstrate homogeneous and 2-point geostatistical models are inadequate in interpreting subsurface heterogeneity, both due to their method and data limitations. Further work is being done with object-based models to produce a system of meandering rivers based on the geological concept of Shenhua injection site. This will help offset the data limitation and bring our model closer to geologic reality.

54 ENVIRONMENTAL SCIENCES↗