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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Optical Control of Adaptive Nanoscale Domain Networks

Adaptive networks can sense and adjust to dynamic environments to optimize their performance. Understanding their nanoscale responses to external stimuli is essential for applications in nanodevices and neuromorphic computing. However, it is challenging to image such responses on the nanoscale with crystallographic sensitivity. Here, the evolution of nanodomain networks in (PbTiO 3 ) n /(SrTiO 3 ) n superlattices (SLs) is directly visualized in real space as the system adapts to ultrafast repetitive optical excitations that emulate controlled neural inputs. The adaptive response allows the system to explore a wealth of metastable states that are previously inaccessible. Their reconfiguration and competition are quantitatively measured by scanning x-ray nanodiffraction as a function of the number of applied pulses, in which crystallographic characteristics are quantitatively assessed by assorted diffraction patterns using unsupervised machine-learning methods. The corresponding domain boundaries and their connectivity are drastically altered by light, holding promise for light-programable nanocircuits in analogy to neuroplasticity. Phase-field simulations elucidate that the reconfiguration of the domain networks is a result of the interplay between photocarriers and transient lattice temperature. The demonstrated optical control scheme and the uncovered nanoscopic insights open opportunities for the remote control of adaptive nanoscale domain networks.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Adaptive laboratory evolution for increased temperature tolerance of the diatom Nitzschia inconspicua

Abstract Outdoor microalgal cultivation for the production of valuable biofuels and bioproducts typically requires high insolation and strains with high thermal (>37°C) tolerance. While some strains are naturally thermotolerant, other strains of interest require improved performance at elevated temperatures to enhance industrial viability. In this study, adaptive laboratory evolution (ALE) was performed for over 300 days using consecutive 0.5°C temperature increases in a constant temperature incubator to attain greater thermal tolerance in the industrially relevant diatom Nitzschia inconspicua str. Hildebrandi. The adapted strain was able to grow at a constant temperature of 37.5°C; whereas this constant temperature was lethal to the parental control, which had an upper‐temperature boundary of 35.5°C before adaptive evolution. Several high‐temperature clonal isolates were obtained from the evolved population following ALE, and increased temperature tolerance was observed in the clonal, parent, and non‐clonal adapted cultures. This ALE method demonstrates the development of enhanced industrial algal strains without the production of genetically modified organisms (GMOs).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Comparative genomics provides insights into the cold adaptation of endophytic fungi associated with Deschampsia antarctica

Endophytic fungi from Deschampsia antarctica , the southernmost flowering plant, provide insights into the cold adaptation mechanisms of plant-associated fungi in extreme environments. This study presents the genome sequences and comparative analysis of eight fungal isolates from D. antarctica leaves. These Antarctic fungal isolates were analyzed alongside 121 plant-associated fungal genomes to uncover signatures of adaptation and endophytic specialization. Antarctic endophytes show striking patterns, including reduced genome size (∼26.3 Mb on average), streamlined gene content (∼8844 genes), and notably small secretomes (∼288 proteins). Despite this reduced gene repertoire, they maintain a robust set of genes encoding carbohydrate-active enzymes (CAZymes) but lack those for lignin and bacterial cell wall degradation, indicating a symbiotic lifestyle that avoids host damage and predation. One isolate, Alternaria sp. UNIPAMPA017 stood out, with 26% of its genome occupied by transposable elements. Lifestyle, rather than phylogeny, was the main driver of CAZyme and secretome profiles, underscoring ecological convergence. Compared to endophytes from Arabidopsis and Populus, D. antarctica endophytes harbor fewer pectin-degrading enzymes, reflecting their adaptation to the cell wall structure of their monocot host. Together, these fungi reveal a pattern of genomic reduction and functional fine-tuning, hallmarks of life adapted to persist in cold, nutrient-scarce niches.

Ascomycota↗
DOE OSTI2022

Adaptive elasticity policies for staging-based in situ visualization

In situ processing aims to alleviate the growing gap between computation and I/O capabilities by performing data processing close to the data source. In situ processing is widely used to process data generated by multiple data sources, including observation data from edge devices or scientific observational facilities and the simulation data generated by scientific computation on a high-performance computing (HPC) platform. For a scientific workflow that is run on an HPC platform and composed of a simulation program and an in situ data analytics or visualization (abbreviated as ana/vis) task, there is an implicit assumption that the computing resources assigned to the workflow keep static during the workflow execution. However, with the converging trend between the HPC and cloud computing platform, running the in situ ana/vis task in an elastic way is promising to decrease its overhead and improve its resource utilization rate. Resource elasticity represents the ability to change resource configurations such as the number of computing nodes/processes during workflow execution. An elastic job may dynamically adjust resource configurations; it may use a few resources at the beginning and more resources toward the end of the job when interesting data appear. However, it is hard to predict a priori how many computing nodes/processes need to be added/removed during the workflow execution to adapt to changing workflow needs. How to efficiently guide elasticity operations, such as growing or shrinking the number of processes used for in situ analysis during workflow execution, is an open-ended research question. In this article, we present adaptive elasticity policies that adopt workflow runtime information collected during workflow execution to predict how to trigger the addition/removal of processes in order to minimize in situ processing overhead. Taking in situ visualization tasks as an example, we integrate the presented elasticity policies into a staging-based elastic workflow and evaluate its efficiency in multiple elasticity scenarios. Compared with the situation without elasticity or with a static elasticity policy that uses a fixed number of processes for each rescaling operation, the adaptive elasticity policy can save overhead in finding a proper resource configuration and improve resource utilization efficiency. Furthermore, one experiment illustrates that the adaptive elasticity policy saves 41% of core-hours compared with the situation without the resource elasticity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Investigating uncertainties in human adaptation and their impacts on water scarcity in the Colorado river Basin, United States

The Colorado River Basin (CRB) supports the water supply for seven states and forty million people in the Western United States (US) and has been suffering an extensive drought for more than two decades. As climate change continues to reshape water resources distribution in the CRB, its impact can differ in intensity and location, resulting in variations in human adaptation behaviors. The feedback from human systems in response to the environmental changes and the associated uncertainty is critical to water resources management, especially for water-stressed basins. This paper investigates how human adaptation affects water scarcity uncertainty in the CRB and highlights the uncertainties in human behavior modeling. Our focus is on agricultural water consumption, as approximately 80% of the water consumption in the CRB is used in agriculture. We adopted a coupled agent-based and water resources modeling approach for exploring human-water system dynamics, in which an agent is a human behavior model that simulates a farmer’s water consumption decisions. We examined uncertainties at the system, agent, and parameter levels through uncertainty, clustering, and sensitivity analyses. The uncertainty analysis results suggest that the CRB water system may experience 13 to 30 years of water shortage during the 2019–2060 simulation period, depending on the paths of farmers’ adaptation. The clustering analysis identified three decision-making classes: bold, prudent, and forward-looking, and quantified the probabilities of an agent belonging to each class. The sensitivity analysis results indicated agents whose decision-making models require further investigation and the parameters with the higher uncertainty reduction potentials. Here, by conducting numerical experiments with the coupled model, this paper presents quantitative and qualitative information about farmers’ adaptation, water scarcity uncertainties, and future research directions for improving human behavior modeling.

Agent-based modeling↗
DOE OSTI2024

Adaptive Data-Driven Deep-Learning Surrogate Model for Frontal Polymerization in Dicyclopentadiene

Frontal polymerization (FP) is a self-sustaining curing process that enables rapid and energy-efficient manufacturing of thermoset polymers and composites. Computational methods conventionally used to simulate the FP process are time-consuming, and repeating simulations are required for sensitivity analysis, uncertainty quantification, or optimization of the manufacturing process. Here, in this work, we develop an adaptive surrogate deep-learning model for FP of dicyclopentadiene (DCPD), which predicts the evolution of temperature and degree of cure orders of magnitude faster than the finite-element method (FEM). The adaptive algorithm provides a strategy to select training samples efficiently and save computational costs by reducing the redundancy of FEM-based training samples. The adaptive algorithm calculates the residual error of the FP governing equations using automatic differentiation of the deep neural network. A probability density function expressed in terms of the residual error is used to select training samples from the Sobol sequence space. The temperature and degree of cure evolution of each training sample are obtained by a 2D FEM simulation. The adaptive method is more efficient and has a better prediction accuracy than the random sampling method. With the well-trained surrogate neural network, the FP characteristics (front speed, shape, and temperature) can be extracted quickly from the predicted temperature and degree-of-cure fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Anion transport as a target of adaption to perchlorate in sulfate-reducing communities

Inhibitors can be used to control the functionality of microbial communities by targeting specific metabolisms. The targeted inhibition of dissimilatory sulfate reduction limits the generation of toxic and corrosive hydrogen sulfide across several industrial systems. Sulfate-reducing microorganisms (SRM) are specifically inhibited by sulfate analogs, such as perchlorate. Previously, we showed pure culture SRM adaptation to perchlorate stress through mutation of the sulfate adenylyltransferase, a central enzyme in the sulfate reduction pathway. Here, we explored adaptation to perchlorate across unconstrained SRM on a community scale. We followed natural and bio-augmented sulfidogenic communities through serial transfers in increasing concentrations of perchlorate. Our results demonstrated that perchlorate stress altered community structure by initially selecting for innately more resistant strains. Isolation, whole-genome sequencing, and molecular biology techniques allowed us to define subsequent genetic mechanisms of adaptation that arose across the dominant adapting SRM. Changes in the regulation of divalent anion:sodium symporter family transporters led to increased intracellular sulfate to perchlorate ratios, allowing SRM to escape the effects of competitive inhibition. Thus, in contrast to pure-culture results, SRM in communities cope with perchlorate stress via changes in anion transport and its regulation. Finally, this highlights the value of probing evolutionary questions in an ecological framework, bridging the gap between ecology, evolution, genomics, and physiology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Quantifying the local adaptive landscape of a nascent bacterial community

Abstract The fitness effects of all possible mutations available to an organism largely shape the dynamics of evolutionary adaptation. Yet, whether and how this adaptive landscape changes over evolutionary times, especially upon ecological diversification and changes in community composition, remains poorly understood. We sought to fill this gap by analyzing a stable community of two closely related ecotypes (“L” and “S”) shortly after they emerged within the E. coli Long-Term Evolution Experiment (LTEE). We engineered genome-wide barcoded transposon libraries to measure the invasion fitness effects of all possible gene knockouts in the coexisting strains as well as their ancestor, for many different, ecologically relevant conditions. We find consistent statistical patterns of fitness effect variation across both genetic background and community composition, despite the idiosyncratic behavior of individual knockouts. Additionally, fitness effects are correlated with evolutionary outcomes for a number of conditions, possibly revealing shifting patterns of adaptation. Together, our results reveal how ecological and epistatic effects combine to shape the adaptive landscape in a nascent ecological community.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Fine-scale contemporary recombination variation and its fitness consequences in adaptively diverging stickleback fish

Despite deep evolutionary conservation, recombination rates vary greatly across the genome and among individuals, sexes and populations. Yet the impact of this variation on adaptively diverging populations is not well understood. Here we characterized fine-scale recombination landscapes in an adaptively divergent pair of marine and freshwater populations of threespine stickleback from River Tyne, Scotland. Through whole-genome sequencing of large nuclear families, we identified the genomic locations of almost 50,000 crossovers and built recombination maps for marine, freshwater and hybrid individuals at a resolution of 3.8 kb. We used these maps to quantify the factors driving variation in recombination rates. We found strong heterochiasmy between sexes but also differences in recombination rates among ecotypes. Hybrids showed evidence of significant recombination suppression in overall map length and in individual loci. Recombination rates were lower not only within individual marine–freshwater-adaptive loci, but also between loci on the same chromosome, suggesting selection on linked gene ‘cassettes’. Through temporal sampling along a natural hybrid zone, we found that recombinants showed traits associated with reduced fitness. Our results support predictions that divergence in cis-acting recombination modifiers, whose functions are disrupted in hybrids, may play an important role in maintaining differences among adaptively diverging populations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

The Marchantia polymorpha pangenome reveals ancient mechanisms of plant adaptation to the environment

Plant adaptation to terrestrial life started 450 million years ago and has played a major role in the evolution of life on Earth. The genetic mechanisms allowing this adaptation to a diversity of terrestrial constraints have been mostly studied by focusing on flowering plants. Here, we gathered a collection of 133 accessions of the model bryophyte Marchantia polymorpha and studied its intraspecific diversity using selection signature analyses, a genome-environment association study and a pangenome. We identified adaptive features, such as peroxidases or nucleotide-binding and leucine-rich repeats (NLRs), also observed in flowering plants, likely inherited from the first land plants. The M. polymorpha pangenome also harbors lineage-specific accessory genes absent from seed plants. We conclude that different land plant lineages still share many elements from the genetic toolkit evolved by their most recent common ancestor to adapt to the terrestrial habitat, refined by lineage-specific polymorphisms and gene family evolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

A variational framework for residual-based adaptivity in neural PDE solvers and operator learning

Residual-based adaptive strategies are widely used in scientific machine learning yet remain largely heuristic. We introduce a variational framework that formalizes these methods through convex transformations of the residual, where different transformations correspond to distinct objective functionals. For instance, exponential weights target uniform error minimization, while linear weights recover quadratic error minimization. This perspective reveals adaptive weighting as a means of selecting sampling distributions that optimize a primal objective, directly linking discretization choices to error metrics. This principled approach yields three key benefits: it enables systematic design of adaptive schemes, reduces discretization error by lowering estimator variance, and enhances learning dynamics by improving gradient signal-to-noise ratio. Extending the framework to operator learning, we demonstrate substantial performance gains across diverse optimizers and architectures. Our results provide a theoretical perspective for residual-based adaptivity and establish a foundation for principled discretization and training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Numerical study of magnetic island coalescence using magnetohydrodynamics with adaptively embedded particle-in-cell model

Collisionless magnetic reconnection typically requires kinetic treatment that is, in general, computationally expensive compared to fluid-based models. In this study, we use the magnetohydrodynamics with an adaptively embedded particle-in-cell (MHD-AEPIC) model to study the interaction of two magnetic flux ropes. This innovative model embeds one or more adaptive PIC regions into a global MHD simulation domain such that the kinetic treatment is only applied in regions where the kinetic physics is prominent. We compare the simulation results among three cases: (1) MHD with adaptively embedded PIC regions, (2) MHD with statically (or fixed) embedded PIC regions, and (3) a full PIC simulation. The comparison yields good agreement when analyzing their reconnection rates and magnetic island separations as well as the ion pressure tensor elements and ion agyrotropy. In order to reach good agreement among the three cases, large adaptive PIC regions are needed within the MHD domain, which indicates that the magnetic island coalescence problem is highly kinetic in nature, where the coupling between the macro-scale MHD and micro-scale kinetic physics is important.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Learning to learn by using nonequilibrium training protocols for adaptable materials

Evolution in time-varying environments naturally leads to adaptable biological systems that can easily switch functionalities. Advances in the synthesis of environmentally responsive materials therefore open up the possibility of creating a wide range of synthetic materials which can also be trained for adaptability. We consider high-dimensional inverse problems for materials where any particular functionality can be realized by numerous equivalent choices of design parameters. By periodically switching targets in a given design algorithm, we can teach a material to perform incompatible functionalities with minimal changes in design parameters. We exhibit this learning strategy for adaptability in two simulated settings: elastic networks that are designed to switch deformation modes with minimal bond changes and heteropolymers whose folding pathway selections are controlled by a minimal set of monomer affinities. The resulting designs can reveal physical principles, such as nucleation-controlled folding, that enable such adaptability.

Science & Technology - Other Topics↗
DOE OSTI2025

Prioritizing urban heat adaptation infrastructure based on multiple outcomes: Comfort, health, and energy

Globally, cities face increasing extreme heat, impacting comfort, health, and energy consumption. Infrastructure-based heat adaptation strategies can improve these outcomes, but each strategy has a unique mix of benefits and drawbacks. Here, we apply an urbanized meteorological model (WRF) with the newly integrated multilayer BEP-Tree street tree model to dynamically downscale Earth System Model projections and a 3-D microclimate model (TUF-Pedestrian) to simulate the street-scale radiation environment impacting pedestrians. We evaluate the performance of five heat adaptation strategies (street trees, cool roofs, green roofs, rooftop photovoltaics (PV), and reflective pavements) during extreme heat events in three cities with contrasting background climates (Toronto, Phoenix, and Miami), under contemporary and end-of-century projected climates, based on three metrics: outdoor heat stress, air conditioning (AC) energy use, and ventilation of vehicular air pollution. No single adaptation strategy improves all three outcomes. While street trees inhibit ventilation, they reduce outdoor heat stress four times more effectively than the next best strategy via shade provision, fully offsetting heat stress increases under a high-emissions end-of-century climate scenario in all cities studied. Cool roofs and green roofs moderately reduce heat stress and energy use. Alternatively, rooftop PV with energy storage can generate sufficient power for space cooling but have marginal effects on heat stress. Reflective pavements are the least effective across metrics. Where the ventilation of street-level emissions is of less concern, our results clearly support the combination of street trees and rooftop PV as a highly complementary and effective means of adaptive mitigation across different climates and neighborhood densities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Drosophila melanogaster pigmentation demonstrates adaptive phenotypic parallelism over multiple spatiotemporal scales

Abstract Populations are capable of responding to environmental change over ecological timescales via adaptive tracking. However, the translation from patterns of allele frequency change to rapid adaptation of complex traits remains unresolved. We used abdominal pigmentation in Drosophila melanogaster as a model phenotype to address the nature, genetic architecture, and repeatability of rapid adaptation in the field. We show that D. melanogaster pigmentation evolves as a highly parallel and deterministic response to shared environmental variation across latitude and season in natural North American populations. We then experimentally evolved replicate, genetically diverse fly populations in field mesocosms to remove any confounding effects of demography and/or cryptic structure that may drive patterns in wild populations; we show that pigmentation rapidly responds, in parallel, in fewer than 15 generations. Thus, pigmentation evolves concordantly in response to spatial and temporal climatic axes. We next examined whether phenotypic differentiation was associated with allele frequency change at loci with established links to genetic variance in pigmentation in natural populations. We found that across all spatial and temporal scales, phenotypic patterns were associated with variation at pigmentation-related loci, and the sets of genes we identified at each scale were largely nonoverlapping. Therefore, our findings suggest that parallel phenotypic evolution is associated with distinct components of the polygenic architecture shifting across each environmental axis to produce redundant adaptive patterns.

Evolutionary Biology↗
DOE OSTI2025

Footprints of Worldwide Adaptation in Structured Populations of Drosophila melanogaster Through the Expanded DEST 2.0 Genomic Resource

Abstract Large-scale genomic resources can place genetic variation into an ecologically informed context. To advance our understanding of the population genetics of the fruit fly Drosophila melanogaster, we present an expanded release of the community-generated population genomics resource Drosophila Evolution over Space and Time (DEST 2.0; https://dest.bio/). This release includes 530 high-quality pooled libraries from flies collected across six continents over more than a decade (2009 to 2021), most at multiple time points per year; 211 of these libraries are sequenced and shared here for the first time. We used this enhanced resource to elucidate several aspects of the species' demographic history and identify novel signs of adaptation across spatial and temporal dimensions. For example, we showed that the spatial genetic structure of populations is stable over time, but that drift due to seasonal contractions of population size causes populations to diverge over time. We identified signals of adaptation that vary between continents in genomic regions associated with xenobiotic resistance, consistent with independent adaptation to common pesticides. Moreover, by analyzing samples collected during spring and fall across Europe, we provide new evidence for seasonal adaptation related to loci associated with pathogen response. Furthermore, we have also released an updated version of the DEST genome browser. This is a useful tool for studying spatiotemporal patterns of genetic variation in this classic model system.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Genomic insights into local adaptation and migration success in reintroduced Coho Salmon of the Wenatchee River basin

ABSTRACT Objective Reintroduction of salmonids into regions where they have been extirpated is a common conservation strategy that is often implemented through natural recolonization, translocation of natural populations, or hatchery-based programs. Locally adapting to specific environmental conditions is critical for long-term population viability, particularly for species like Coho Salmon Oncorhynchus kisutch, which face diverse selective pressures during their migration. This study focused on the mid-Columbia River Coho Salmon reintroduction program managed by Yakama Nation Fisheries, which has successfully reintroduced Coho Salmon into the Wenatchee and Methow River basins, Washington. Notably, these populations have adapted to the longer migration route than those in the founding stock, with selection favoring individuals with an earlier arrival time and that can navigate a 15-km, high-gradient canyon to reach optimal spawning grounds. The objectives of this study were to investigate whether specific genomic regions are under selection for traits associated with return location and timing in Coho Salmon. Methods Low-coverage whole-genome resequencing data were used to screen for genomic regions associated with the phenotypes of interest. Results A weak polygenic signal in female Coho Salmon was found to be associated with return group, with a subset of candidate adaptive regions occurring across eight chromosomes. Conclusions These findings provide insights into the genomic mechanisms underlying local adaptation in reintroduced salmon populations and inform broodstock selection strategies aimed at promoting natural production and long-term population sustainability.

Horn, Rebekah L.↗
DOE OSTI2021

Adaptive variational quantum eigensolvers for highly excited states

Highly excited states of quantum many-body systems are central objects in the study of quantum dynamics and thermalization that challenge classical computational methods due to their volume-law entanglement content. In this work, we explore the potential of variational quantum algorithms to approximate such states. We propose an adaptive variational quantum eigensolver (VQE) for excited states (X) that self-generates a variational ansatz for arbitrary eigenstates of a many-body Hamiltonian H by attempting to minimize the energy variance with respect to H. We benchmark the method by applying it to an Ising spin chain with integrable and nonintegrable regimes, where we calculate various quantities of interest, including the total energy, magnetization density, and entanglement entropy. We also compare the performance of adaptive VQE-X to an adaptive variant of the folded-spectrum method. For both methods, we find a strong dependence of the algorithm's performance on the choice of operator pool used for the adaptive construction of the ansatz. In particular, an operator pool including long-range two-body gates accelerates the convergence of both algorithms in the nonintegrable regime. Here, we also study the scaling of the number of variational parameters with system size, finding that an exponentially large number of parameters may be necessary to approximate individual highly excited states. Nevertheless, we argue that these methods lay a foundation for the use of quantum algorithms to study finite-energy-density properties of many-body systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗