Results for “hypothesis tests” — Page 25

DOE OSTI2022

Machine Intelligence-Centered System for Automated Characterization of Functional Materials and Interfaces

Classic design of experiment relies on a time-intensive workflow that requires planning, data interpretation, and hypothesis building by experienced researchers. Here, in this paper, we describe an integrated, machine-intelligent experimental system which enables simultaneous dynamic tests of electrical, optical, gravimetric, and viscoelastic properties of materials under a programmable dynamic environment. Specially designed software controls the experiment and performs on-the-fly extensive data analysis and dynamic modeling, real-time iterative feedback for dynamic control of experimental conditions, and rapid visualization of experimental results. The system operates with minimal human intervention and enables time-efficient characterization of complex dynamic multifunctional environmental responses of materials with simultaneous data processing and analytics. The system provides a viable platform for artificial intelligence (AI)-centered material characterization, which, when coupled with an AI-controlled synthesis system, could lead to accelerated discovery of multifunctional materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Simulations of field emitters and multipacting in PIP-II Single Spoke Resonator Type-2

It has been found in benchmark tests that some Single Spoke Resonator Type-2 (SSR2) cavities have early field emission onset as well as strong multipacting barriers. A longstanding hypothesis is that field-emitted electrons in the high electric field accelerating gap can migrate and ignite multipacting bands in the low electric field regions of the cavity periphery. In this study, we use simulation techniques to examine multipacting behavior in SSR2 cavities from electrons seeded in common field emitter locations. Additionally, we investigated seed locations for areas in SSR2 cavities which may have poor coverage during high pressure water rinsing and compared the multipacting behavior.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Testing and Characterization to Develop a Mechanistic Explanation for Unsaturated Drift of Fiber Optic Sensors during High-Dose Irradiation

The primary limitation for any optical fiber-based sensor for nuclear reactor applications is radiation-induced attenuation (RIA) of the transmitted and/or reflected signals. Based on several recent studies, RIA is tolerable for some fused silica optical fibers with the proper choice of sensing wavelength and fiber dopants. For extreme temperature applications (> 1000°C), sapphire optical fibers have been proposed; however, recent optical transmission measurements performed on bulk sapphire samples showed prohibitively large RIA. For some sensors, radiation-induced dimensional changes in the fiber materials can also cause significant drift. Moreover, the drift that was observed in numerous experiments performed in the High Flux Isotope Reactor (HFIR), the Advanced Test Reactor, the Massachusetts Institute of Technology Reactor, and other international facilities far exceeded what would be expected based on compaction of fused silica glass. Clearly, additional work is needed to better understand the origins of both RIA and radiation-induced drift in both silica and sapphire optical fiber-based sensors before these sensors can be reliably deployed for nuclear applications. This work evaluated the underlying mechanisms that may be responsible for RIA and drift in silica and sapphire materials. First, detailed characterization was performed on bulk fused silica glass samples that were previously irradiated to different neutron fluences at different temperatures to better understand the structural changes that drive radiation-induced drift in the absence of coating effects that are discussed later. Results show that the non-monotonic compaction that occurs with increasing neutron fluence continues up to fast neutron fluences approaching 10 22 n/cm 2 , which has important implications for physics-based models that may be used to compensate for the sensor drift. Initial Raman spectroscopy and synchrotron x-ray diffraction provide insights into the nature of the structural changes. Next, detailed characterizations were performed on silica fibers with various coatings that were subjected to several different thermal treatments. The hypothesis is that the coatings convert to carbon-rich materials that compact under irradiation, putting a large compressive strain on the fiber. Out-of-pile testing confirms that both polyimide and acrylate fiber coatings convert to glassy carbon (GC) materials when heated under inert conditions, and the degree of order (i.e., graphitization) increases with increasing temperature. The results provide increasingly strong evidence that the combination of polymeric coatings and inert (or vacuum) conditions render fiber optic sensors susceptible to significant radiation-induced drift that would not otherwise exist in uncoated fibers. Finally, transmission electron microscopy was performed on bulk sapphire samples that were irradiated to two neutron fluences at different temperatures to gain insights into the potential mechanisms driving the prohibitive RIA at higher neutron fluences and temperatures. Contrary to previous hypotheses, results show that scattering from radiation-induced voids cannot explain the observed RIA. Similarly, models for scattering losses from dislocation loops also do not agree with the experimental results. Instead, fitting to the experimental data shows that increased absorption from aluminum vacancy centers is the most likely explanation for the the prohibitively large RIA that was observed at high irradiation temperature and dose. In addition, the voids that formed in these single-crystal samples were found to align along the basal plane (a-axis) as opposed to that seen in previous observations of c-axis alignment in polycrystalline samples, which could have important implications for anisotropic swelling and other phenomena that could affect sensor performance at high neutron fluence.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Testing and Characterization to Develop a Mechanistic Explanation for Unsaturated Drift of Fiber Optic Sensors during High-Dose Irradiation

The primary limitation for any optical fiber-based sensor for nuclear reactor applications is radiation-induced attenuation (RIA) of the transmitted and/or reflected signals. Based on several recent studies, RIA is tolerable for some fused silica optical fibers with the proper choice of sensing wavelength and fiber dopants. For extreme temperature applications (> 1000°C), sapphire optical fibers have been proposed; however, recent optical transmission measurements performed on bulk sapphire samples showed prohibitively large RIA. For some sensors, radiation-induced dimensional changes in the fiber materials can also cause significant drift. Moreover, the drift that was observed in numerous experiments performed in the High Flux Isotope Reactor (HFIR), the Advanced Test Reactor, the Massachusetts Institute of Technology Reactor, and other international facilities far exceeded what would be expected based on compaction of fused silica glass. Clearly, additional work is needed to better understand the origins of both RIA and radiation-induced drift in both silica and sapphire optical fiber-based sensors before these sensors can be reliably deployed for nuclear applications. This work evaluated the underlying mechanisms that may be responsible for RIA and drift in silica and sapphire materials. First, detailed characterization was performed on bulk fused silica glass samples that were previously irradiated to different neutron fluences at different temperatures to better understand the structural changes that drive radiation-induced drift in the absence of coating effects that are discussed later. Results show that the non-monotonic compaction that occurs with increasing neutron fluence continues up to fast neutron fluences approaching 10 22 n/cm 2 , which has important implications for physics-based models that may be used to compensate for the sensor drift. Initial Raman spectroscopy and synchrotron x-ray diffraction provide insights into the nature of the structural changes. Next, detailed characterizations were performed on silica fibers with various coatings that were subjected to several different thermal treatments. The hypothesis is that the coatings convert to carbon-rich materials that compact under irradiation, putting a large compressive strain on the fiber. Out-of-pile testing confirms that both polyimide and acrylate fiber coatings convert to glassy carbon (GC) materials when heated under inert conditions, and the degree of order (i.e., graphitization) increases with increasing temperature. The results provide increasingly strong evidence that the combination of polymeric coatings and inert (or vacuum) conditions render fiber optic sensors susceptible to significant radiation-induced drift that would not otherwise exist in uncoated fibers. Finally, transmission electron microscopy was performed on bulk sapphire samples that were irradiated to two neutron fluences at different temperatures to gain insights into the potential mechanisms driving the prohibitive RIA at higher neutron fluences and temperatures. Contrary to previous hypotheses, results show that scattering from radiation-induced voids cannot explain the observed RIA. Similarly, models for scattering losses from dislocation loops also do not agree with the experimental results. Instead, fitting to the experimental data shows that increased absorption from aluminum vacancy centers is the most likely explanation for the the prohibitively large RIA that was observed at high irradiation temperature and dose. In addition, the voids that formed in these single-crystal samples were found to align along the basal plane (a-axis) as opposed to that seen in previous observations of c-axis alignment in polycrystalline samples, which could have important implications for anisotropic swelling and other phenomena that could affect sensor performance at high neutron fluence.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Dual Function Solid State Battery with Self-Forming Self-Healing Electrolyte and Separator (Final Scientific/Technical Report)

Over the course of this program, we have demonstrated an improved rechargeable Li/I2 battery with reduced impedance, improved cyclability, improved Coulombic Efficiency, and the ability to “self-heal” during shorting events. Multi-year efforts have led to the development and improvements in the composition, fabrication, and preparation of the solid state electrolyte, the role of the interface, and design of appropriate test conditions. These investigations have led to significant improvements in Coulombic efficiency. By systematic development of a composite solid state electrolyte, this system is able to self-form upon charge, thus lending itself to long shelf life before use. The initial project objective was to demonstrate a solid-state rechargeable battery based on a Li-metal anode and iodine cathode with a self-forming, self-healing electrolyte and separator with high gravimetric and volumetric energy density. The final deliverables of this project build on a multi-year effort beginning with materials development and design to generate a suitable solid state electrolyte composite with demonstrated higher ionic conductivity compared to the baseline (LiI). Development in cell design and interface modification leading to the generation of rechargeable solid electrolyte cells with Coulombic efficiency improvements of ~3X over initial tests, as well as demonstration of self-healing behavior, where the cells retained their ability to continue extended cycling after shorting events. The hypothesis put forward as part of the initial concept for the project was that the cell chemistry would be self-healing. For example, if a lithium dendrite formed during charge of the cell and reached the iodine cathode, the reaction of the lithium dendrite with the iodine would consume the tip of the dendrite to form lithium iodide, more of the solid electrolyte. The formation of the solid electrolyte would allow the cell to recover and continue to function. This type of self-healing during cycling has been demonstrated and has the potential to improve on safety considerations that stem from shorting events in other Li- containing systems.

25 ENERGY STORAGE↗

DOE OSTI2024

Season of drought affects growth, but not nonstructural carbohydrates dynamics, in Pinus taeda saplings

In temperate evergreen conifers, growth occurs mostly in summer but photosynthesis proceeds year-round; thus, nonstructural carbohydrates (NSCs) increase in winter but decrease in summer. Given that mild drought reduces growth but not photosynthesis, a drought in summer should increase NSCs more than one in winter. However, the active regulation hypothesis suggests that to increase future drought resilience, plants might downregulate growth to increase NSCs after a winter drought even if NSCs do not increase during the drought. To test whether this is so, potted Pinus taeda saplings (age year) were subjected to 6-month droughts in a greenhouse with one treatment receiving drought during winter (September–March), and another during summer (March–September). Here, both treatments were compared with a control. To measure dry biomass and NSCs, we harvested plants monthly following each drought, while to assess changes in growth rates, we measured height and diameter monthly. While we observed seasonal variation and an overall increase during the study, we found no drought-related changes in NSC dynamics; however, drought did reduce growth. Furthermore, drought in winter did reduce growth during the following summer, but the reduction was less than for a drought in summer. We conclude that the effect of drought on NSCs was too small to detect in our plants. While better control of soil water would have reduced a major source of uncertainty, plants with larger NSC reserves or more intense stress would also yield easier-to-detect effects. Although not definitive, our results suggest that water stress does not lead to dramatic changes in seasonal NSC dynamics in its aftermath, despite what one might expect under the active regulation hypothesis.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries

Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and electron/probe microscopy. This, in turn, necessitates the development of active learning methods capable of exploring relevant parameter spaces with the smallest number of steps. In this work, an active learning approach based on conavigation of the hypothesis and experimental spaces is introduced. This is realized by combining the structured Gaussian processes containing probabilistic models of the possible system's behaviors (hypotheses) with reinforcement learning policy refinement (discovery). This approach closely resembles classical human-driven physical discovery, when several alternative hypotheses realized via models with adjustable parameters are tested during an experiment. This approach is demonstrated for exploring concentration-induced phase transitions in combinatorial libraries of Sm-doped BiFeO 3 using piezoresponse force microscopy, but it is straightforward to extend it to higher-dimensional parameter spaces and more complex physical problems once the experimental workflow and hypothesis generation are available.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Neurosensory and Sinus Evolution as Tyrannosauroid Dinosaurs Developed Giant Size: Insight from the Endocranial Anatomy of Bistahieversor sealeyi

Tyrannosaurus rex and other tyrannosaurid dinosaurs were apex predators during the latest Cretaceous, which combined giant size and advanced neurosensory systems. Computed tomography (CT) data have shown that tyrannosaurids had a trademark system of a large brain, large olfactory bulbs, elongate cochlear ducts, and expansive endocranial sinuses surrounding the brain and sense organs. Older, smaller tyrannosauroid relatives of tyrannosaurids developed some, but not all, of these features, raising the hypothesis that tyrannosaurid-style brains evolved before the enlarged tyrannosaurid-style sinuses, which might have developed only with large body size. This has been difficult to test, however, because little is known about the brains and sinuses of the first large-bodied tyrannosauroids, which evolved prior to Tyrannosauridae. Here we present the first CT data for one of these species, Bistahieversor sealeyi from New Mexico. Bistahieversor had a nearly identical brain and sinus system as tyrannosaurids like Tyrannosaurus, including a large brain, large olfactory bulbs, reduced cerebral hemispheres, and optic lobes, a small tab-like flocculus, long and straight cochlear ducts, and voluminous sinuses that include a supraocciptal recess, subcondyar sinus, and an anterior tympanic recess that exits the braincase via a prootic fossa. When characters are plotted onto tyrannosauroid phylogeny, there is a two-stage sequence in which features of the tyrannosaurid-style brain evolved first (in smaller, nontyrannosaurid species like Timurlengia), followed by features of the tyrannosaurid-style sinuses (in the first large-bodied nontyrannosaurid tyrannosauroids like Bistahieversor). This suggests that the signature tyrannosaurid sinus system evolved in concert with large size, whereas the brain did not.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

How to improve scaling from traits to ecosystem processes

Scaling approaches in ecology assume that traits are the main attributes by which organisms influence ecosystem functioning. However, several recent empirical papers have found only weak links between traits and ecosystem functioning, questioning the usefulness of trait-based ecology (TBE). Here, we argue that these studies often suffer from one or more widespread misconceptions. Specifically, these studies often (i) conflict with the conceptual foundations of TBE, (ii) lack theory- or hypothesis-driven selection and use of traits, (iii) tend to ignore intraspecific variation, and (iv) use experimental or study designs that are not well suited to make strong tests of TBE assumptions. Addressing these aspects could significantly improve our ability to scale from traits to ecosystem functioning.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Sugar loading is not required for phloem sap flow in maize plants

We report phloem transport of photoassimilates from leaves to non-photosynthetic organs, such as the root and shoot apices and reproductive organs, is crucial to plant growth and yield. For nearly 90 years, evidence has been generally consistent with the theory of a pressure-flow mechanism of phloem transport. Central to this hypothesis is the loading of osmolytes, principally sugars, into the phloem to generate the osmotic pressure that propels bulk flow. Here we used genetic and light manipulations to test whether sugar import into the phloem is required as the driving force for phloem sap flow. Using carbon-11 radiotracer, we show that a maize sucrose transporter1 (sut1) loss-of-function mutant has severely reduced export of carbon from photosynthetic leaves (only ~4% of the wild type level). Yet, the mutant remarkably maintains phloem pressure at ~100% and sap flow speeds at ~50–75% of those of wild type. Potassium (K+) abundance in the phloem was elevated in sut1 mutant leaves. Fluid dynamic modelling supports the conclusion that increased K+ loading compensated for decreased sucrose loading to maintain phloem pressure, and thereby maintained phloem transport via the pressure-flow mechanism. Furthermore, these results suggest that sap flow and transport of other phloem-mobile nutrients and signalling molecules could be regulated independently of sugar loading into the phloem, potentially influencing carbon–nutrient homoeostasis and the distribution of signalling molecules in plants encountering different environmental conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2023

The Effects of Lithium Ions and pH on the Function of Polyacrylic Acid Binder for Silicon Anodes

Binder plays a critical role in the performance of silicon anodes for lithium-ion batteries, specifically by connecting particles of active material and promoting adhesion to the current collector. Recent studies have differed on the relative cycle life of silicon anodes made from water-based polyacrylic acid (PAA) vs LiOH-PAA binders. Differences between the two may be due to the pH value or the extra Li + in the binder, both of which change when LiOH is added to PAA. Here we investigate the impact of these two variables on the performance of silicon anodes. Regarding the effect of Li + , cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) results confirm our hypothesis that the extra Li + facilitates ion transport. Regarding pH, we find that high pH in binders is detrimental to the electrode mechanical integrity, as observed in peeling tests and cross-sectional imaging. However, viscosity tests reveal that increased pH benefits the coating and mixing process. Further, our cycling results show that LiOH-PAA binder maintains greater cell capacity than does PAA, and further that LiOH-PAA at pH 4.5 leads to a cell with the highest capacity. Therefore, an intermediate pH is an optimal compromise between benefits observed for the low and high pH experiments.

25 ENERGY STORAGE↗

DOE OSTI2024

Methods and stability tests associated with the sterile neutrino search using improved high-energy ν μ event reconstruction in IceCube

We provide supporting details for the search for a 3 + 1 sterile neutrino using data collected over 10.7 years at the IceCube Neutrino Observatory. The analysis uses atmospheric muon-flavored neutrinos from 0.5 to 100 TeV that traverse Earth to reach the IceCube detector and finds a best-fit point at sin 2 ( 2 θ 24 ) = 0.16 and Δ m 41 2 = 3.5 eV 2 with a goodness-of-fit p value of 12% and consistency with the null hypothesis of no oscillations to sterile neutrinos with a p value of 3.1%. Several improvements were made over past analyses, which are reviewed in this article, including upgrades to the reconstruction and the study of sources of systematic uncertainty. We provide details of the fit quality and discuss stability tests that split the data for separate samples, comparing results. We find that the fits are consistent between split datasets. Published by the American Physical Society 2024

Abbasi, R.↗

DOE OSTI2026

Validation Studies of RELAP5-3D for High-Temperature Gas-Cooled Reactor Analysis Using a Refined RELAP5-3D Model of the High Temperature Test Facility

RELAP5-3D has been used extensively for high-temperature gas-cooled reactor (HTGR) analysis, but such analysis lies outside the validation basis for the code. Oregon State University (OSU) and the United States Department of Energy’s (DOE) Advanced Reactor Technologies (ART) – Gas-cooled reactor campaign worked together to design and operate the High Temperature Test Facility (HTTF) at OSU to provide validation data for prismatic HTGR thermal hydraulics analysis. Work using a previous RELAP5-3D model of HTTF demonstrated the ability to reproduce trends in HTTF data but an inability to reproduce the values measured in multiple experiments within the uncertainty. It was hypothesized that a new model with a finer radial nodalization would perform better for HTTF analysis. In this paper, we present the development of that more refined model and analyze HTTF experiments PG-27 and PG-29, a pressurized (PCC) and depressurized conduction cooldown (DCC) transient. Comparing PG-27 results to experimental data shows a better prediction of steady state and transient temperatures; however, a deeper dive reveals that better transient temperature predictions are a result of better initial conditions. The transient temperature rise in the core is comparable between the two models, but the legacy model predicts temperatures in the reflectors better. The new model demonstrates the ability to predict unique temperatures in each sector of the core during PG-29, capturing the azimuthal asymmetry; however, the temperature predictions still differ from measured temperatures. One hypothesis is that differences between the assumed and actual power distribution leads to the underprediction of the individual block temperatures. These results suggest that gaps and uncertainties in the measured boundary conditions for the HTTF tests do not allow to fully validate RELAP5-3D for prismatic HTGR analyses. The authors suspect that some of the differences between predicted and measured temperatures may be a result of uncertainties associated with HTTF itself rather than RELAP5-3D. We demonstrated improved validation for steady-state analysis, but significantly improved transient performance has yet to be recognized.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Graph-based machine learning improves just-in-time defect prediction

The increasing complexity of today’s software requires the contribution of thousands of developers. This complex collaboration structure makes developers more likely to introduce defect-prone changes that lead to software faults. Determining when these defect-prone changes are introduced has proven challenging, and using traditional machine learning (ML) methods to make these determinations seems to have reached a plateau. In this work, we build contribution graphs consisting of developers and source files to capture the nuanced complexity of changes required to build software. By leveraging these contribution graphs, our research shows the potential of using graph-based ML to improve Just-In-Time (JIT) defect prediction. We hypothesize that features extracted from the contribution graphs may be better predictors of defect-prone changes than intrinsic features derived from software characteristics. We corroborate our hypothesis using graph-based ML for classifying edges that represent defect-prone changes. This new framing of the JIT defect prediction problem leads to remarkably better results. We test our approach on 14 open-source projects and show that our best model can predict whether or not a code change will lead to a defect with an F1 score as high as 77.55% and a Matthews correlation coefficient (MCC) as high as 53.16%. This represents a 152% higher F1 score and a 3% higher MCC over the state-of-the-art JIT defect prediction. We describe limitations, open challenges, and how this method can be used for operational JIT defect prediction.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Time-series learning of latent-space dynamics for reduced-order model closure

In this work, we study the performance of long short-term memory networks (LSTMs) and neural ordinary differential equations (NODEs) in learning latent-space representations of dynamical equations for an advection-dominated problem given by the viscous Burgers equation. Our formulation is devised in a nonintrusive manner with an equation-free evolution of dynamics in a reduced space with the latter being obtained through a proper orthogonal decomposition. In addition, we leverage the sequential nature of learning for both LSTMs and NODEs to demonstrate their capability for closure in systems that are not completely resolved in the reduced space. We assess our hypothesis for two advection-dominated problems given by the viscous Burgers equation. We observe that both LSTMs and NODEs are able to reproduce the effects of the absent scales for our test cases more effectively than does intrusive dynamics evolution through a Galerkin projection. This result empirically suggests that time-series learning techniques implicitly leverage a memory kernel for coarse-grained system closure as is suggested through the Mori–Zwanzig formalism.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Sensitivities of subgrid-scale physics schemes, meteorological forcing, and topographic radiation in atmosphere-through-bedrock integrated process models: a case study in the Upper Colorado River basin

Abstract. Mountain hydrology is controlled by interacting processes extending from the atmosphere through the bedrock. Integrated process models (IPMs), one of the main tools needed to interpret observations and refine conceptual models of the mountainous water cycle, require meteorological forcing that simulates the atmospheric process to predict hydroclimate then subsequently impacts surface–subsurface hydrology. Complex terrain and extreme spatial heterogeneity in mountainous environments drive uncertainty in several key considerations in IPM configurations and require further quantification and sensitivity analyses. Here, we present an IPM using the Weather Research and Forecasting (WRF) model which forces an integrated hydrologic model, ParFlow-CLM, implemented over a domain centered over the East River watershed (ERW), located in the Upper Colorado River basin (UCRB). The ERW is a heavily instrumented 300 km2 region in the headwaters of the UCRB near Crested Butte, CO, with a growing atmosphere-through-bedrock observation network. Through a series of experiments in the water year 2019 (WY19), we use four meteorological forcings derived from commonly used reanalysis datasets, three subgrid-scale physics scheme configurations in WRF, and two terrain shading options within WRF to test the relative importance of these experimental design choices for key hydrometeorological metrics including precipitation and snowpack, as well as evapotranspiration, groundwater storage, and discharge simulated by the ParFlow-CLM. Our hypothesis is that uncertainty from synoptic-scale forcings produces a much larger spread in surface–subsurface hydrologic fields than subgrid-scale physics scheme choice. Results reveal that the WRF subgrid-scale physics configuration leads to larger spatiotemporal variance in simulated hydrometeorological conditions, whereas variance across meteorological forcing with common subgrid-scale physics configurations is more spatiotemporally constrained. Despite reasonably simulating precipitation, a delay in simulated discharge peak is due to a systematic cold bias across WRF simulations, suggesting the need for bias correction. Discharge shows greater variance in response to the WRF simulations across subgrid-scale physics schemes (26 %) rather than meteorological forcing (6 %). The topographic radiation option has minor effects on the watershed-average hydrometeorological processes but adds profound spatial heterogeneity to local energy budgets (±30 W m−2 in shortwave radiation and 1 K air temperature differences in late summer). This is the first presentation of sensitivity analyses that provide support to help guide the scientific community to develop observational constraints on atmosphere-through-bedrock processes and their interactions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A Mass Conservation Relaxed (MCR) LSTM Model for Streamflow Simulation Across CONUS

The recent development of the physics-aware Mass-Conserving Long Short-Term Memory network (MC-LSTM) provides an alternative to other data-driven Deep Learning (DL) models in hydrology. Mass-Conserving Long Short-Term Memory incorporates mass conservation directly into the LSTM architecture. Despite the theoretical advancements, studies have reported a surprisingly limited performance of the MC-LSTM in streamflow simulation. We hypothesize that such a limitation is due to the unrealistic mass conservation scheme in MC-LSTM, which overlooks unobserved incoming water fluxes beyond precipitation. As an attempt to verify this hypothesis, we propose a Mass Conservation Relaxed LSTM (MCR-LSTM), which incorporates a bi-directional mass relaxation (MR) component to account for potential incoming water fluxes beyond precipitation. We train and test the proposed MCR-LSTM model across 531 watersheds in the contiguous United States (CONUS) against three baseline models: the Sacramento Soil Moisture Accounting, LSTM, and MC-LSTM. Our results show that MCR-LSTM outperforms MC-LSTM despite its underperformance compared to LSTM. Specifically, MCR-LSTM's advantage over MC-LSTM is mainly seen in the Plains and Western U.S., where the newly incorporated MR component better simulates water loss and suggests the likely existence of additional incoming water fluxes beyond precipitation, respectively. The novelty and contribution of this study are twofold: firstly, it introduces an alternative physics-aware DL tool (i.e., MCR-LSTM) in hydrology with higher accuracy in specific regions compared to MC-LSTM. Secondly, it provides a diagnosis of regions where strict, precipitation-based mass conservation constraints may be unrealistic in streamflow simulation.

deep learning↗

DOE OSTI2020

Pattern of perturbations from a coherent quantum inflationary horizon

It is proposed that if quantum states of space-time are coherent on null surfaces, holographic Planck-scale fluctuations of inflationary horizons dominate the formation of primordial scalar curvature perturbations. It is shown that the reduction of quantum states on nearly-spherical emergent horizon surfaces around each observer creates a distinctive pattern whose correlations in the angular domain differ from the standard quantum theory of inflation. Causal constraints are used in a semiclassical model to formulate candidate directional symmetries. It is suggested that this hypothesis could provide a physical explanation for several well known anomalies measured in CMB anisotropy. New exact symmetries are predicted, such as a vanishing temperature correlation function at 90 degrees angular separation, that can be tested with current data.

79 ASTRONOMY AND ASTROPHYSICS↗