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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Many-Body Potential for Simulating Self-Assembly of Polymer-Grafted Nanoparticles in a Polymer Matrix

Many-body interactions between polymer-grafted nanoparticles (NPs) play a key role in promoting their assembly into low-dimensional structures within polymer melts, even when the particles are spherical and isotropically grafted. However, capturing such interactions in simulations of NP assembly is very challenging because explicit modeling of the polymer grafts and melt chains is highly computationally expensive, even using coarse-grained models. Here, we develop a many-body potential for describing the effective interactions between spherical polymer-grafted NPs in a polymer matrix through a machine-learning approach. The approach involves using permutationally invariant polynomials to fit two- and three-body interactions derived from the potential of mean force calculations. The potential developed here reduces the computational cost by several orders of magnitude, thereby, allowing us to explore assembly behavior over large length and time scales. We show that the potential not only reproduces previously known assembled phases such as 1D strings and 2D hexagonal sheets, which generally cannot be achieved using isotropic two-body potentials, but can also help discover interesting phases such as networks, clusters, and gels. We demonstrate how each of these assembly morphologies intrinsically arises from a competition between two- and three-body interactions. Our approach for deriving many-body effective potentials can be readily extended to other colloidal systems, enabling researchers to make accurate predictions of their behavior and dissect the role of individual interaction energy terms of the overall potential in the observed behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Magnetic properties of CrX 3 (X = Cl, Br, I) monolayers in excited states

The presence of intrinsic ferromagnetism in a new class of magnetic semiconductors CrX 3 (X = Cl, Br, I) in a reduced dimension is associated with magnetic anisotropy energy (MAE). MAE is relevant for the magnetic order of CrX 3 . The control of magnetism in such 2D materials through external stimuli is gaining importance in view of potential technological applications. Here, we use all-electron density functional theory to investigate the magnetism of monolayers of chromium halides in excited states using a cluster model. We study the magnetization reversal barrier for two types of electronic singly excited states: (a) spin-preserving transitions, and (b) spin-flip transitions. While all the three halides show enhancement of MAE in excited states, the MAEs are significant for CrI 3 due to the large spin–orbit coupling in the iodines. The changes in the MAE in the excited state are influenced by the local charge distribution surrounding the iodine atoms. Using a model system, we also show that the MAE in chromium tri-iodides is strongly influenced by the charge transfer to the iodines in an excited state. Furthermore, controlling the charge transfer to the iodines can potentially be used to control the magnetization reversal barrier.

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

Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation

Abstract This study proposes and assesses a methodology to obtain high-quality probabilistic predictions and uncertainty information of near-landfall tropical cyclone–driven (TC-driven) storm tide and inundation with limited time and resources. Forecasts of TC track, intensity, and size are perturbed according to quasi-random Korobov sequences of historical forecast errors with assumed Gaussian and uniform statistical distributions. These perturbations are run in an ensemble of hydrodynamic storm tide model simulations. The resulting set of maximum water surface elevations are dimensionality reduced using Karhunen–Loève expansions and then used as a training set to develop a polynomial chaos (PC) surrogate model from which global sensitivities and probabilistic predictions can be extracted. The maximum water surface elevation is extrapolated over dry points incorporating energy head loss with distance to properly train the surrogate for predicting inundation. We find that the surrogate constructed with third-order PCs using elastic net penalized regression with leave-one-out cross validation provides the most robust fit across training and test sets. Probabilistic predictions of maximum water surface elevation and inundation area by the surrogate model at 48-h lead time for three past U.S. landfalling hurricanes (Irma in 2017, Florence in 2018, and Laura in 2020) are found to be reliable when compared to best track hindcast simulation results, even when trained with as few as 19 samples. The maximum water surface elevation is most sensitive to perpendicular track-offset errors for all three storms. Laura is also highly sensitive to storm size and has the least reliable prediction. Significance Statement The purpose of this study is to develop and evaluate a methodology that can be used to provide high-quality probabilistic predictions of hurricane-induced storm tide and inundation with limited time and resources. This is important for emergency management purposes during or after the landfall of hurricanes. Our results show that sampling forecast errors using quasi-random sequences combined with machine learning techniques that fit polynomial functions to the data are well suited to this task. The polynomial functions also have the benefit of producing exact sensitivity indices of storm tide and inundation to the forecasted hurricane properties such as path, intensity, and size, which can be used for uncertainty estimation. The code implementing the presented methodology is publicly available on GitHub.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Plasma Guns for Magnetized Fuel Targets for PJMIF

In plasma jet driven magneto-inertial fusion (PJMIF) an array of discrete supersonic plasma jets is used to form a spherically imploding plasma liner, which then compresses a magnetized plasma target to fusion conditions. With funding from ARPA-E’s ALPHA program and from Strong Atomics LLC, HyperJet Fusion Corp and HyperV Technologies Corp. previously developed the plasma guns required for an experimental demonstration of the plasma liner formation part of the concept. A 36-gun demonstration of an imploding spherical plasma liner is currently underway on the PLX facility at Los Alamos National Laboratory. This present project addresses the next step required for a complete PJMIF concept, i.e. developing the magnetized plasma target. We proposed to form the target by stagnating a number of magnetized plasma jets in the center of the target chamber. This is accomplished by adding a bias field coil to the plasma liner gun to form a magnetized plasma jet. This experimental development took place at HyperJet in a geometry replicating the bias field environment that will be seen on a PLX port so the results are directly transferrable to the PLX experiment. The objective of this effort was the technical development and characterization of a new magnetized plasma jet using a high-performance, high momentum flux, contoured-gap coaxial plasma gun appropriate for use on the next stage of the PLX experiment. Electromagnetic modeling of the coil indicated the coil was best placed around the aluminum tubes of the gun transmission line, rather than around the chamber port as originally proposed. This maximized field strength in the breech and yielded much better flux linkage between gun electrodes. A 30-turn coil was ultimately implemented, allowing long pulses that could diffuse through the metal walls on the timescale of interest. \machtwo modeling predicted that less capacitance in the main PFN could potentially improve plasma jet velocities due to better matching of the current to the smaller plasma mass and the existing electrode contour designed to suppress blowby. This proved true, as testing showed markedly improved performance when the original 600uF bank was reduced to 400uF. A number of diagnostics were built and/or upgraded in order to characterize the plasma jets, inluding laser interferomtery for density, photodiodes for velocity, Bdot probes for magnetic field measurements, a Triple probe for temperature measurements, and spectroscopy for impurity content. A plasma gun with the 30-turn magnet coil installed, a 70% reduction in gas valve plenum volume, and a 33% reduction in main PFN capacitance produced a dense, high velocity, well magnetized plasma jet. We met or exceeded virtually all of the plasma jet parameter goals. Peak velocity of 135 km/s exceeded the 100 km/s goal by 35%, while the peak density of over 1.0x10 15 cm -3 was 3.3 times the goal of >3.0x10 14 cm -3 . Shot-to-shot repeatability is excellent, with a jitter of less than 300ns observed on the arrival fronts of the photodiode signals from one shot to the next. Average plasma jet lengths of 33cm (at 120km/s) were slightly longer than the 20cm goal, but jets as short as 11.8cm were observed at 135km/s. Mass is much higher than the targeted goal, averaging 106ug per shot compared to 20ug. The magnetic field is still a bit lower than desired, with a maximum to date of ~811G, compared to the goal of 1000G. Average values, though, were typically in the 300-450G range, when measured further downstream after some expected in flight decay. Temperature measurements are still a work in progress. Increasing the B field and completing temperature measurements will be continued on into the ongoing BETHE project.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Coordinated operation of electricity and natural gas systems from day-ahead to real-time markets

Power systems worldwide are becoming more reliant on energy from natural gas, wind, and solar, posing possible reliability and coordination challenges from the tighter coupling of these infrastructure systems. This paper proposes a framework for the market-based coordination of electricity and natural gas system operations. The proposed framework includes a power system model that accounts for flexibility in the commitment of power plants with short start-up and shut-down times, coupled with a dynamic gas model that simulates when gas cannot be delivered to generators. The capabilities of the framework are illustrated using real-world electric power and gas systems, including scenarios around wind and solar penetrations and the analysis of time-variant, “shaped flow” gas nominations. Our results indicate that coordination between power and gas systems improves total gas delivery and reduces out-of-merit order dispatch in the electricity system, and that shaped flows may reduce unserved gas in systems with high penetrations of wind and solar. Coordination can have mixed effects on carbon-dioxide emissions, with emissions increasing with coordination for current systems during high load weeks but decreasing for systems with high renewable penetrations, particularly during periods of high variability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Quantifying heavy quark transport coefficients with an improved transport model

We report that the heavy-flavor transport coefficients contain important information on the strong interaction at finite temperatures. The extraction of these numbers from experimental data requires dynamical modeling of heavy-flavor transport that is coupled to realistic medium evolution. Furthermore, meaningful extractions necessitate both a faithful implementation of the physical inputs to be tested and the quantification of model uncertainty. For these purposes, we have developed a partonic transport model LIDO. It has an improved treatment of in-medium parton bremsstrahlung, which has been calibrated to theoretical calculations in a simple medium to reduce modeling uncertainty. Regarding the interaction between heavy quark and the medium, few-body perturbative scatterings are applied to large-momentum transfer (q) processes, while a diffusion equation models the dynamics of small-q processes. Such a separation restricts the explicit use of medium quasi-particles to large-q processes only. Another advantage is that deviations from the leading-order probe-medium coupling can be parametrized as an additional contribution to the diffusion constant. The heavy quark transport coefficients are then extracted with uncertainty estimation from a Bayesian analysis including both the RHIC and the LHC data. The results are found to be consistent with earlier extraction of the light-quark transport coefficients at high momentum and be comparable with lattice calculations of the heavy-flavor diffusion constant in the static limit at low momentum.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Investigating the impact of cloud-radiative feedbacks on tropical precipitation extremes

Abstract Although societally important, extreme precipitation is difficult to represent in climate models. This study shows one robust aspect of extreme precipitation across models: extreme precipitation over tropical oceans is strengthened through a positive feedback with cloud-radiative effects. This connection is shown for a multi-model ensemble with experiments that make clouds transparent to longwave radiation. In all cases, tropical extreme precipitation reduces without cloud-radiative effects. Qualitatively similar results are presented for one model using the cloud-locking method to remove cloud feedbacks. The reduced extreme precipitation without cloud-radiative feedbacks does not arise from changes in the mean climate. Rather, evidence is presented that cloud-radiative feedbacks enhance organization of convection and most extreme precipitation over tropical oceans occurs within organized systems. This result suggests that climate models must correctly predict cloud structure and properties, as well as capture the essence of organized convection in order to accurately represent extreme rainfall.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Subsidence Measurements at the Teller 27 Field Site, Seward Peninsula, Alaska, 2021

In September 2021, differential GPS (dGPS) points were collected in areas of visual subsidence in the Teller watershed near mile marker 27 of the Bob Blodgett Nome-Teller Memorial Highway on the Seward Peninsula of Alaska. This study aimed to understand how structural permafrost loss is affecting meter-scale ground elevation. As ice thaws, heaving and slumping of the landscape creates new microtopographical features on the landscape that may alter surface hydrology and soil moisture and thus plant community composition and biogeochemical cycling. The Teller 27 field site is underlain with discontinuous permafrost, so the landscape features a variety of permafrost features. We collected ground elevations from eight subsidence areas in two forms: 1) Transects were sampled laterally through areas of ground subsidence to capture the high edges and low spots where the earth slumped. 2) The circumference of the subsidence area was sampled in order to measure the ground area affected by subsidence. These data were collected with an Emlid Reach RS2 dGPS and a base station. This dataset includes one *.csv of dGPS locations of the eight subsidence locations and one *.kml of measurement locations.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Power and Natural Gas Systems Interface Study

Power systems worldwide are becoming more reliant on energy from natural gas, wind, and solar, posing potential reliability and coordination challenges from the tighter coupling of these infrastructure systems. This paper proposes a framework for the market-based coordination of electricity and natural gas system operations. The proposed framework includes a power system model that accounts for flexibility in the commitment of power plants with short start-up and shut-down times, coupled with a dynamic gas model that simulates when gas cannot be delivered to generators. The capabilities of the framework are illustrated using real-world electric power and gas systems, including scenarios around wind and solar penetrations and the analysis of time-variant, “shaped flow” gas nominations. Our results indicate that coordination between power and gas systems improves total gas delivery and reduces out-of-merit order dispatch in the electricity system, and that shaped flows may reduce unserved gas in systems with high penetrations of wind and solar.

coordinated operation↗
DOE OSTI2020

Tritium Transport Phenomena in Molten-Salt Reactors

In this work, we review phenomena relevant to tritium transport in molten-salt reactors, which produce tritium from lithium and beryllium salts at significantly higher levels than other reactor types. Modeling of such phenomena began following MSRE operations, and these early models attempted to predict measured tritium distributions in the MSRE, accounting for turbulent mass-transport processes (using established heat transfer correlations), permeation through a variety of metal structures such as heat exchanger tubes, and transport to and from bubbles introduced into the salt by gas sparging. The models reasonably reproduced the MSRE data, but did so best when the permeability of structures was reduced by about a factor of 1,000. This issue does not appear to have been conclusively resolved, and all of the more recent attempts to model tritium transport in molten salts appear to make use of the same methodology. MSRE remains, however, essentially our only source of integral tritium transport data relevant to MSRs. Here, we generalize the MSRE approach to permeation in order to include potential rate-limiting effects at interfaces, as well the effects of added hydrogen. Appropriately non-dimensionalized, this system of equations identifies two dimensionless numbers whose relative values clearly delineate the conditions under which mass transport, surface effects, permeation, and hydrogen swamping are expected to become rate-limiting. We also describe the preliminary conceptual design of a forced convection FLiBe loop, into which tritium would be introduced for the purpose of providing validation data for such a model. The primary purpose of this is to investigate the coupled transport phenomena described above and identify those that are rate-limiting in MSRs. Additional test section configurations are described that would address other transport phenomena relevant to MSRs, including bubbly flows and graphite interactions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Saccharomycotina yeasts defy long-standing macroecological patterns

The Saccharomycotina yeasts (“yeasts” hereafter) are a fungal clade of scientific, economic, and medical significance. Yeasts are highly ecologically diverse, found across a broad range of environments in every biome and continent on earth; however, little is known about what rules govern the macroecology of yeast species and their range limits in the wild. Here, we trained machine learning models on 12,816 terrestrial occurrence records and 96 environmental variables to infer global distribution maps at ~1 km2 resolution for 186 yeast species (~15% of described species from 75% of orders) and to test environmental drivers of yeast biogeography and macroecology. We found that predicted yeast diversity hotspots occur in mixed montane forests in temperate climates. Diversity in vegetation type and topography were some of the greatest predictors of yeast species richness, suggesting that microhabitats and environmental clines are key to yeast diversity. We further found that range limits in yeasts are significantly influenced by carbon niche breadth and range overlap with other yeast species, with carbon specialists and species in high-diversity environments exhibiting reduced geographic ranges. Finally, yeasts contravene many long-standing macroecological principles, including the latitudinal diversity gradient, temperature-dependent species richness, and a positive relationship between latitude and range size (Rapoport’s rule). These results unveil how the environment governs the global diversity and distribution of species in the yeast subphylum. These high-resolution models of yeast species distributions will facilitate the prediction of economically relevant and emerging pathogenic species under current and future climate scenarios.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Synergistically combining peracetic acid and reduced graphene oxide membranes to degrade trace organic contaminants

The removal of trace organic contaminants is a critical step for the reuse of municipal and industrial wastewater. Herein, we demonstrate a catalytic membrane platform synergistically integrating reduced graphene oxide (rGO) membranes and peracetic acid (PAA) oxidation, using a dye of methylene blue (MB) and a pesticide of 2,4-dichlorophenoxyacetic acid (2,4-D) as model contaminants. In a crossflow system with rGO membrane and 25 ppm PAA, the degradation rates of MB and 2,4-D were 22 and 0.12 g h −1 per g rGO, with an initial concentration of 10 and 1.1 ppm, respectively. The degradation time profile of MB and 2,4-D follows a pseudo-first-order model, and the degradation rate constant increases with increasing initial PAA doses. Here, the rGO membranes also exhibited good stability over a 1-month test for 2,4-D degradation. In addition to the nanofiltration performance of the rGO membrane, the integrated PAA-rGO membrane process shows great potential to degrade various organic contaminants without the need to separate and recover the metal-free catalyst in downstream treatment.

2,4-D↗
DOE OSTI2021

Efficiency and mechanisms of Sb(III/V) removal by Fe-modified biochars using X-ray absorption spectroscopy

Fe-modified biochars (FeBC) are effective antimony (Sb) removal materials; however, the removal mechanisms require further investigation. In this study, aqueous Sb(III) and Sb(V) removal by FeBC (300, 600, and 900 °C) was evaluated, with the adsorption mechanisms investigated using X-ray absorption spectroscopy (XAS). Screening results (based on removal efficiencies) using different types of FeBC indicated the 900 °C FeCl3- modified biochar (FeCl3BC900) achieved the best Sb(III/V) removal performance. The kinetics of the Sb(III/V) removal process were best fitted by a pseudo-second-order model. Additionally, the isothermal results were described by Langmuir and Redlich-Peterson models. Aqueous analysis and X-ray absorption near-edge structure data fitting indicated Sb(III) was oxidized to Sb(V) in the Sb(III)-spiked system, and the oxidation extent increased with increasing pyrolysis temperature. The oxidation process rapidly occurred in both the solution and biochar. No Sb(V) was reduced to Sb(III) in the Sb(V)-spiked system. The XAS results of the isothermal experiment indicated the oxidation capacity of FeCl3BC900 was limited for high initial Sb(III) concentrations. The SbFe1 and Sb-Fe2 bonding distances were 3.05–3.10 and 3.47–3.54 Å, respectively, indicating inner-sphere complexes were formed during the Sb(III/V) removal processes. The Sb(III/V) removal mechanisms included electrostatic adsorption, inner-sphere complexes, and coprecipitation. Oxidation was also involved in Sb(III) removal.

Antimony↗
DOE OSTI2020

A comparison of computational models for predicting yield sooting index

Sooting propensity, a measurement of how much particulate matter is produced when a fuel is burned, is a property of significant interest among researchers who are striving to discover the next generation of cleaner, more efficient fuels and fuel additives. Many compounds are not viable as fuels and/or fuel additives, and as a result, designing cleaner-burning biofuels using only experimental techniques is inefficient. Predictive models have been instrumental in reducing this inherent difficulty, providing researchers with a tool to preemptively screen compounds before production and testing. The present work compares the accuracies and interpretabilities of existing models used to predict a particular measure of sooting propensity, Yield Sooting Index (YSI). These models include artificial neural networks, graph neural networks, and multivariate equations. A novel equation for predicting YSI based on atom path count and bond order is proposed, which can highlight key structural components that contribute to YSI. It was found that artificial neural networks slightly outperform graph neural networks and greatly outperform multivariate equations in blind (test set) prediction accuracy; however, graph neural networks and multivariate equations provide significantly more interpretability as to how compound structure relates to YSI. Predictions of YSI are compared to experimental measurements for previously un-tested compounds with cetane numbers comparable to diesel fuel (50-60) (butyl decanoate, ethyl decanoate, 1,4-bis(ethenoxymethyl)cyclohexane, and 5-heptyloxolan-2-one), and it was found that these compounds produce significantly less soot compared to diesel fuel.

09 BIOMASS FUELS↗
DOE OSTI2025

Impacts of Bulk Microphysics Scheme Structural Choices on Simulations of Rain Initiation Through Drop Coalescence

This study examines how different structural choices in bulk microphysics schemes impact the simulation of warm rain initiation. A single liquid category (SLC) approach prognosing up to four moments of a single drop size distribution (DSD) is compared to the traditional two-category, two-moment approach with separate DSDs for cloud and rain (four total prognostic variables). Different methods for calculating tendencies of the prognostic variables from drop collision-coalescence are also tested: a discretized numerical-integration approach, machine learning via neural networks, lookup tables, and traditional power law fits. Relative to simulations using a bin microphysics model, SLC gives smaller error overall than the two-category approach when numerical integration is used to calculate the collision-coalescence tendencies for both. Replacing the numerical integration with a pre-computed lookup table reduces computational cost with little loss of accuracy. However, using fitted power laws with SLC to represent the collision-coalescence tendencies substantially reduces accuracy and leads to an order of magnitude increase in error. It is also demonstrated that with SLC, reasonably accurate solutions are obtained using only three prognostic moments, while a two-moment SLC scheme leads to substantial error. Overall, both the choice of prognostic moments (e.g., SLC vs. two-category) and method to calculate the collision-coalescence tendencies are important to consider for minimizing errors in bulk schemes. SLC with a sufficiently detailed calculation of the collision-coalescence tendencies provides accurate solutions for a reasonable computational cost, providing a viable alternative to the traditional two-category, two-moment approach for bulk microphysics.

320 (cloud physics and chemistry)↗
DOE OSTI2025

A Multichannel Silicon Package for Large-Scale Skipper-CCD Experiments

The next generation of experiments for light-dark-matter and neutrino searches based on skipper charge-coupled devices (skipper-CCDs) introduces new challenges for the sensor packaging and readout architecture. Scaling the active mass while simultaneously reducing the experimental backgrounds in orders of magnitude requires a novel high-density silicon-based package that must be massively produced and tested. In this work, we present a silicon multichip module design capable of hosting up to 16 skipper-CCDs, along with the fabrication process for the first prototypes. We thoroughly tested and characterized the assembled prototypes to build an empirical model for the video output signal of skipper-CCDs integrated into the silicon package. We then used this model in simulations to optimize the fabrication process and achieve the robust performance required for the full-scale array, which we validated through a new round of prototype fabrication. We outline the specifications selected for the ongoing production of 1500 silicon wafers that will ultimately add up to a 10-kg skipper-CCD array with 24000 readout channels.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Neutrino Flavor Model Building and the Origins of Flavor and CP Violation

The neutrino sector offers one of the most sensitive probes of new physics beyond the Standard Model of Particle Physics (SM). The mechanism of neutrino mass generation is still unknown. The observed suppression of neutrino masses hints at a large scale, conceivably of the order of the scale of a rand unified theory (GUT), which is a unique feature of neutrinos that is not shared by the charged fermions. The origin of neutrino masses and mixing is part of the outstanding puzzle of fermion masses and mixings, which is not explained ab initio in the SM. Flavor model building for both quark and lepton sectors is important in order to gain a better understanding of the origin of the structure of mass hierarchy and flavor mixing, which constitute the dominant fraction of the SM parameters. Recent activities in neutrino flavor model building based on non-Abelian discrete flavor symmetries and modular flavor symmetries have been shown to be a promising direction to explore. The emerging models provide a framework that has a significantly reduced number of undetermined parameters in the flavor sector. In addition, such a framework affords a novel origin of CP violation from group theory due to the intimate connection between physical CP transformation and group theoretical properties of non-Abelian discrete groups. Model building based on non-Abelian discrete flavor symmetries and their modular variants enables the particle physics community to interpret the current and anticipated upcoming data from neutrino experiments. Non-Abelian discrete flavor symmetries and their modular variants can result from compactification of a higher-dimensional theory. Pursuit of flavor model building based on such frameworks thus also provides the connection to possible UV completions: in particular, to string theory. We emphasize the importance of constructing models in which the uncertainties of theoretical predictions are smaller than, or at most compatible with, the error bars of measurements in neutrino experiments. While there exist proof-of-principle versions of bottom-up models in which the theoretical uncertainties are under control, it is remarkable that the key ingredients of such constructions were discovered first in top-down model building. We outline how a successful unification of bottom-up and top-down ideas and techniques may guide us towards a new era of precision flavor model building in which future experimental results can give us crucial insights into the UV completion of the SM.

Almumin, Yahya (ORCID:0000000196396772)↗