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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 469 records · Page 26

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DOE OSTI2019

Scientific discovery in a model-centric framework: Reproducibility, innovation, and epistemic diversity

Consistent confirmations obtained independently of each other lend credibility to a scientific result. We refer to results satisfying this consistency as reproducible and assume that reproducibility is a desirable property of scientific discovery. Yet seemingly science also progresses despite irreproducible results, indicating that the relationship between reproducibility and other desirable properties of scientific discovery is not well understood. These properties include early discovery of truth, persistence on truth once it is discovered, and time spent on truth in a long-term scientific inquiry. We build a mathematical model of scientific discovery that presents a viable framework to study its desirable properties including reproducibility. In this framework, we assume that scientists adopt a model-centric approach to discover the true model generating data in a stochastic process of scientific discovery. We analyze the properties of this process using Markov chain theory, Monte Carlo methods, and agent-based modeling. We show that the scientific process may not converge to truth even if scientific results are reproducible and that irreproducible results do not necessarily imply untrue results. The proportion of different research strategies represented in the scientific population, scientists’ choice of methodology, the complexity of truth, and the strength of signal contribute to this counter-intuitive finding. Important insights include that innovative research speeds up the discovery of scientific truth by facilitating the exploration of model space and epistemic diversity optimizes across desirable properties of scientific discovery.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Resolving temperature limitation on spring productivity in an evergreen conifer forest using a model–data fusion framework

Abstract. The flow of carbon through terrestrial ecosystems and the response to climate are critical but highly uncertain processes in the global carbon cycle. However, with a rapidly expanding array of in situ and satellite data, there is an opportunity to improve our mechanistic understanding of the carbon (C) cycle's response to land use and climate change. Uncertainty in temperature limitation on productivity poses a significant challenge to predicting the response of ecosystem carbon fluxes to a changing climate. Here we diagnose and quantitatively resolve environmental limitations on the growing-season onset of gross primary production (GPP) using nearly 2 decades of meteorological and C flux data (2000–2018) at a subalpine evergreen forest in Colorado, USA. We implement the CARbon DAta-MOdel fraMework (CARDAMOM) model–data fusion network to resolve the temperature sensitivity of spring GPP. To capture a GPP temperature limitation – a critical component of the integrated sensitivity of GPP to temperature – we introduced a cold-temperature scaling function in CARDAMOM to regulate photosynthetic productivity. We found that GPP was gradually inhibited at temperatures below 6.0 ∘C (±2.6 ∘C) and completely inhibited below −7.1 ∘C (±1.1 ∘C). The addition of this scaling factor improved the model's ability to replicate spring GPP at interannual and decadal timescales (r=0.88), relative to the nominal CARDAMOM configuration (r=0.47), and improved spring GPP model predictability outside of the data assimilation training period (r=0.88). While cold-temperature limitation has an important influence on spring GPP, it does not have a significant impact on integrated growing-season GPP, revealing that other environmental controls, such as precipitation, play a more important role in annual productivity. This study highlights growing-season onset temperature as a key limiting factor for spring growth in winter-dormant evergreen forests, which is critical in understanding future responses to climate change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

A reference high-pressure CH4 adsorption isotherm for zeolite Y: results of an interlaboratory study

Abstract This paper reports the results of an international interlaboratory study led by the National Institute of Standards and Technology (NIST) on the measurement of high-pressure surface excess methane adsorption isotherms on NIST Reference Material RM 8850 (Zeolite Y), at 25 °C up to 7.5 MPa. Twenty laboratories participated in the study and contributed over one-hundred adsorption isotherms of methane on Zeolite Y. From these data, an empirical reference equation was determined, along with a 95% uncertainty interval ( U k =2 ). By requiring participants to replicate a high-pressure reference isotherm for carbon dioxide adsorption on NIST Reference Material RM 8852 (ZSM-5), this interlaboratory study also demonstrated the usefulness of reference isotherms in evaluating the performance of high-pressure adsorption experiments.

Nguyen, H. G. T. (ORCID:0000000210527565)↗

DOE OSTI2021

Coupled physics simulation of fracture in nuclear fuel pellets induced by resistive heating

While it is well known that the UO 2 fuel used in light-water reactors cracks early in its life in the reactor, it is very difficult to collect in-reactor data regarding fuel cracking initiation and propagation to validate fracture models in fuel performance codes. Most fracture data comes from the post-irradiation examination of fuel that has been subjected to long and complex power histories. Replicating the thermal gradients experienced in this fuel to study crack initiation and propagation in a laboratory environment is challenging. This is primarily because the fuel is heated volumetrically by fission, which is difficult to replicate. One way to approximate this volumetric heating out of the reactor is to use electrical resistance (or Joule heating). An electrical conductivity model has been developed and coupled with the existing thermomechanical modeling capability in the BISON nuclear fuel performance code. This will permit simulation of experiments that use resistive heating for the purpose of validating fracture models in BISON. The modeling approach developed here is applied to simulate both historical and current resistive heating experiments on ceramic nuclear fuel.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2019

Modeling pulse-cleaning plasma mirrors from dielectric response to saturation: A particle-in-cell approach

Pulse-cleaning plasma mirrors are widely employed to improve ultraintense laser contrast, but much of the literature concerning their effect on the reflected pulse is empirical. A simulation study of pulse-cleaning plasma mirrors using the particle-in-cell code large scale plasma is presented here. The importance of capturing initial ionization from neutral atoms, collisional effects, and simulation dimensionality is considered. Excellent agreement with experimental data is obtained when a multiphoton ionization model is employed. Furthermore, a series of 2D simulations is shown to accurately replicate both the reflected light intensity and mode obtained from full 3D simulations at significantly reduced computation cost.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Museomics Dissects the Genetic Basis for Adaptive Seasonal Coloration in the Least Weasel

Abstract Dissecting the link between genetic variation and adaptive phenotypes provides outstanding opportunities to understand fundamental evolutionary processes. Here, we use a museomics approach to investigate the genetic basis and evolution of winter coat coloration morphs in least weasels (Mustela nivalis), a repeated adaptation for camouflage in mammals with seasonal pelage color moults across regions with varying winter snow. Whole-genome sequence data were obtained from biological collections and mapped onto a newly assembled reference genome for the species. Sampling represented two replicate transition zones between nivalis and vulgaris coloration morphs in Europe, which typically develop white or brown winter coats, respectively. Population analyses showed that the morph distribution across transition zones is not a by-product of historical structure. Association scans linked a 200-kb genomic region to coloration morph, which was validated by genotyping museum specimens from intermorph experimental crosses. Genotyping the wild populations narrowed down the association to pigmentation gene MC1R and pinpointed a candidate amino acid change cosegregating with coloration morph. This polymorphism replaces an ancestral leucine residue by lysine at the start of the first extracellular loop of the protein in the vulgaris morph. A selective sweep signature overlapped the association region in vulgaris, suggesting that past adaptation favored winter-brown morphs and can anchor future adaptive responses to decreasing winter snow. Using biological collections as valuable resources to study natural adaptations, our study showed a new evolutionary route generating winter color variation in mammals and that seasonal camouflage can be modulated by changes at single key genes.

Miranda, Inês↗

DOE OSTI2024

Complete genomes of Asgard archaea reveal diverse integrated and mobile genetic elements

Asgard archaea are of great interest as the progenitors of Eukaryotes, but little is known about the mobile genetic elements (MGEs) that may shape their ongoing evolution. Here, we describe MGEs that replicate in Atabeyarchaeia, a wetland Asgard archaea lineage represented by two complete genomes. We used soil depth–resolved population metagenomic data sets to track 18 MGEs for which genome structures were defined and precise chromosome integration sites could be identified for confident host linkage. Additionally, we identified a complete 20.67 kbp circular plasmid and two family-level groups of viruses linked to Atabeyarchaeia, via CRISPR spacer targeting. Closely related 40 kbp viruses possess a hypervariable genomic region encoding combinations of specific genes for small cysteine-rich proteins structurally similar to restriction-homing endonucleases. One 10.9 kbp integrative conjugative element (ICE) integrates genomically into theAtabeyarchaeum deiterrae-1chromosome and has a 2.5 kbp circularizable element integrated within it. The 10.9 kbp ICE encodes an expressed Type IIG restriction-modification system with a sequence specificity matching an active methylation motif identified by Pacific Biosciences (PacBio) high-accuracy long-read (HiFi) metagenomic sequencing. Restriction-modification of Atabeyarchaeia differs from that of another coexisting Asgard archaea, Freyarchaeia, which has few identified MGEs but possesses diverse defense mechanisms, including DISARM and Hachiman, not found in Atabeyarchaeia. Overall, defense systems and methylation mechanisms of Asgard archaea likely modulate their interactions with MGEs, and integration/excision and copy number variation of MGEs in turn enable host genetic versatility.

Biochemistry & Molecular Biology↗

DOE OSTI2025

Analysis of a Disturbance Event with Inverter-Based Resources Using EMT Simulations

Increasing penetration of inverter-based resources (IBRs) necessitates newer methods of planning and analysis of disturbances. The existing phasor-domain transient stability (TS) analysis may not capture the dynamics of IBRs during fault events. Here, in this paper, electromagnetic transient (EMT) simulations using high-fidelity detailed model of power grid and one of the affected photovoltaic (PV) plants during the Angeles Forest disturbance in 2018 are performed. In these simulations, the processes to develop EMT models of power grid from traditional phasor-domain TS data and PV plant from collected data are described. Thereafter, using these simulations, the response of the PV plant during the fault event in 2018 is replicated and a sensitivity analysis is performed. The sensitivity analysis consists of making changes to the components within the PV plant and in the power grid to evaluate the impact they have on the response observed by the PV plant during the fault event. This analysis provides an understanding of the components that impact the operation of a PV plant during fault events and provide guidance to system planners on the studies that need to be performed to maintain a reliable power grid as new IBR plants are integrated.

42 ENGINEERING↗

DOE OSTI2021

IA-TmG.2.0

Soil samples were collected in triplicate in the Fall of 2017 across three grassland locations having differences in historical annual precipitation. For each of the three field site soil collections, one deeply sequenced composite metagenome (> 1 terabase) was obtained and individually sequenced in replicate to provide three additional terabase metagenomes for replicate statistical comparison in addition to screening for viral soil contigs for assessing the impact of historical annual precipitation. This data package provides new knowledge of viral types, abundances, diversity, and auxiliary metabolic genes (AMGs) in grassland soils for predicting viral response to changes in their environment.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

KS-TmG.2.0

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

WA-TmG.2.0

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

E-Labs - Learning with Authentic Data

the success teachers have had providing an opportunity for students to: • Organize and conduct authentic research. • Experience the environment of scientific collaborations. • Possibly make real contributions to a burgeoning scientific field. We've created projects that are problem-based, student driven and technology dependent. Students reach beyond classroom walls to explore data with other students and experts and share results, publishing original work to a worldwide audience. Students can discover and extend the research of other students, modeling the processes of modern, large-scale research projects. From start to finish e-Labs are student-led, teacher-guided projects. Students need only a Web browser to access computing techniques employed by professional researchers. A Project Map with milestones allows students to set the research plan rather than follow a step-by-step process common in other online projects. Most importantly, e-Labs build the learning experience around the students' own questions and let them use the very tools that scientists use. Students contribute to and access shared data, most derived from professional research databases. They use common analysis tools, store their work and use metadata to discover, replicate and confirm the research of others. This is where real scientific collaboration begins. Using online tools, students correspond with other research groups, post comments and questions, prepare summary reports, and in general participate in the part of scientific research that is often left out of classroom experiments. Teaching tools such as student and teacher logbooks, pre- and post-tests and an assessment rubric aligned with learner outcomes help teachers guide student work. Constraints on interface designs and administrative tools such as registration databases give teachers the "one-stop-shopping" they seek for multiple e-Labs. Teaching and administrative tools also allow us to track usage and assess the impact on student learning.

Bardeen, Marjorie G.↗

DOE OSTI2021

Response to Andrew Fowlie’s Comments

This Letter to the Editor of Harvard Data Science Review is a rejoinder to a Letter to the Editor by Andrew Fowlie, titled “Comment on Reproducibility and Replication of Experimental Particle Physics Results”.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

A methodology to develop multi-physics dynamic fuel cell system models validated with vehicle realistic drive cycle data

Fuel cell (FC) technology has been identified as a technically attractive solution to decarbonize the transportation sector, especially for heavy-duty vehicles. In this context, the industry and the scientific community are in need of advanced fuel cell systems (FCS) models that are able to replicate real -world operating conditions. Due to the scarcity of said models in the open literature, this study aimed to develop a comprehensive methodology to calibrate and validate multi -physics dynamic FCS models. Therefore, the key contribution of this paper is the detailed description of the calibration process for each component and the calibration order. The specific focus here was to accurately describe the behavior of the FC stack as well as the cathode, anode, and cooling circuits of the balance of plant. The model was calibrated with the aid of experimental data from a Toyota Mirai FC electric vehicle, which was predominantly retrieved from the vehicle's Controller Area Network (CAN) bus system thereby negating the need for major intrusion into the powertrain system. The validation process was deemed successful with the model being able to truthfully replicate the characteristics of the FC vehicle operated on the World-wide harmonized Light duty Test Cycle (WLTC) 3b and US06 driving cycle. The time -resolved physical parameters such as the cathode pressure, mass flow, or the FC stack temperature were captured with high fidelity, while the overall performance parameters such as the H2 consumption in the stack and the system, and the compressor energy consumption were predicted accurately with a deviation lower than 0.47%, 1.75% and 1.89% with respect to the experimental data, respectively.

Lopez-Juarez, Marcos↗

DOE OSTI2024

Role of the likelihood for elastic scattering uncertainty quantification

In the last decade, uncertainty quantification (UQ) for optical model potentials (OMPs) has become a focal point for nuclear reaction theory, and several competing approaches for OMP UQ have recently been developed. Here, we clarify recent efforts to compare frequentist and Bayesian approaches in the context of OMP UQ [G. B. King et al., Phys. Rev. Lett. 122, 232502 (2019)]. We replicate a portion of that OMP UQ study but use independent statistical tools. Specifically, we compare two methods for OMP parameter inference from elastic scattering data: the Levenberg-Marquardt algorithm for χ 2 minimization on one hand and Markov chain Monte Carlo (MCMC) sampling on the other. Separately, we assess the common practice of using a renormalized likelihood (χ 2 /N), N being the number of data points, instead of the canonical weighted-least-squares likelihood (χ 2 ), as a way of accounting for unknown data correlations. Here, we show that for a generic linear model and for a five-parameter OMP analysis, frequentist and uniform-prior Bayesian approaches recover the same optimum and uncertainty estimates—not systematically larger uncertainties for the Bayesian approach, as was concluded in G. B. King et al., Phys. Rev. Lett. 122, 232502 (2019). Further, we show that if an additional, near-degenerate parameter is introduced into the same OMP analysis such that the parameter posterior becomes non-Gaussian, then covariance-based estimates of uncertainty become unreliable. Finally, we show that regardless of optimization approach, if χ 2 /N is used for the likelihood, the resulting parametric uncertainties increase by $\sqrt{N}$, and that this is responsible for the conclusions drawn in the revisited study. Based on our replication results, we find that a fortuitous cancellation of unreported errors and the renormalization factor can lead to improvement in empirical coverages, as was the case in the original comparative study. We emphasize that developing and applying a realistic likelihood function is an essential task in a UQ analysis, and that several recent UQ studies that employed a renormalized likelihood (i.e., including a 1/N factor) may have yielded unrealistically large uncertainties for elastic-scattering observables. If the parameter posterior deviates from multivariate-normal, a sampling-based approach like MCMC has a clear advantage over methods that assume the Laplace approximation holds. We note that empirical coverage can serve as an important internal check for the analyst whose model or data may have additional, unaccounted-for uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Fourier Neural Networks as Function Approximators and Differential Equation Solvers

We present a Fourier neural network (FNN) that can be mapped directly to the Fourier decomposition. The choice of activation and loss function yields results that replicate a Fourier series expansion closely while preserving a straightforward architecture with a single hidden layer. The simplicity of this network architecture facilitates the integration with any other higher-complexity networks, at a data pre- or postprocessing stage. We validate this FNN on naturally periodic smooth functions and on piecewise continuous periodic functions. We showcase the use of this FNN for modeling or solving partial differential equations with periodic boundary conditions. The main advantages of the current approach are the validity of the solution outside the training region, interpretability of the trained model, and simplicity of use.

Fourier decomposition↗

DOE OSTI2024

Data-driven Modeling for Grid Edge IBRs: A Digital Twin Perspective of User-Defined Models

Recent events in Odessa have brought attention to the challenges associated with the interaction between Inverter- Based Resources (IBRs) and the transmission and distribution system. The NERC event diagnosis report has highlighted sev- eral issues, emphasizing the need for continuous performance monitoring of these IBRs by system operators. Key areas of concern include the mismatch of control and protection perfor- mance of IBRs between the original equipment manufacturer (OEM)-provided models and field measurements. The inability to replicate the realistic response can result in incorrect reliability and resilience studies. In this paper, we developed an approach on how to emulate the behavior of an IBR using measurement data obtained for system operators to utilize in real-time and long- term planning. Two experiments are conducted in the phasor domain and electromagnetic transients (EMT) domain to emulate the behavior for grid forming and grid following inverters under various operating conditions and the effectiveness of the proposed model is demonstrated in terms of accuracy and ease of utilizing user-defined models (UDMs)

Mahapatra, Kaveri [BATTELLE (PACIFIC NW LAB)]↗

DOE OSTI2021

An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models

Modern machine learning and autonomous experimentation schemes in materials science rely on accurate analysis of the data ingested by these models. Unfortunately, accurate analysis of the underlying data can be difficult, even for domain experts, complicating the training of the models intended to drive experiments. This is especially true when the goal is to identify the presence of weak signatures in diffraction or spectroscopic datasets. In this work, we examine a set of as-obtained diffraction data that track the phase transition from monoclinic to tetragonal in a Nb-doped VO2 film as a function of temperature and dopant concentration. We then task a set of domain experts and a set of machine learning experts with identifying which phase is present in each diffraction pattern manually and algorithmically, respectively; in both cases, the labels can vary dramatically, especially at the phase boundaries. We use the mode of the labels and the Shannon entropy as a method to capture, preserve and propagate consensus labels and their variance. Further we use the expert labels as a benchmark and demonstrate the use of Shannon entropy weighted scoring to test the performance of machine learning generated labels. Finally, we propose a material data challenge centered around generating improved labeling algorithms. This real-world dataset curated with expert labels can act as test bed for new algorithms. The raw data, annotations and code used in this study are all available online at data.gov and the interested reader is encouraged to replicate and improve the existing models

97 MATHEMATICS AND COMPUTING↗