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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on Banshee Distribution Network

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on the Banshee Distribution Network: Preprint

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Pathway Evolution Through a Bottlenecking-Debottlenecking Strategy and Machine Learning-Aided Flux Balancing

The evolution of pathway enzymes enhances the biosynthesis of high-value chemicals, crucial for pharmaceutical, and agrochemical applications. However, unpredictable evolutionary landscapes of pathway genes often hinder successful evolution. Here, the presence of complex epistasis is identifued within the representative naringenin biosynthetic pathway enzymes, hampering straightforward directed evolution. Subsequently, a biofoundry-assisted strategy is developed for pathway bottlenecking and debottlenecking, enabling the parallel evolution of all pathway enzymes along a predictable evolutionary trajectory in six weeks. This study then utilizes a machine learning model, ProEnsemble, to further balance the pathway by optimizing the transcription of individual genes. The broad applicability of this strategy is demonstrated by constructing an Escherichia coli chassis with evolved and balanced pathway genes, resulting in 3.65 g L -1 naringenin. The optimized naringenin chassis also demonstrates enhanced production of other flavonoids. This approach can be readily adapted for any given number of enzymes in the specific metabolic pathway, paving the way for automated chassis construction in contemporary biofoundries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Core Design of the Holos-Quad Microreactor

The Holos-Quad micro-reactor concept, developed by HolosGen LLC, is equipped with a 22 MWt (Mega-Watt thermal) core and an integral power conversion system converting the core thermal energy into approximately 10 MWe (Mega-Watt electric). This design can be configured to support a wide range of applications. It is a very innovative high-temperature gas-cooled reactor concept using TRI-structural ISOtropic particle fuel (TRISO) distributed in graphite hexagonal blocks, cooled with helium in a direct Brayton cycle independently executed by four Subcritical Power Modules (SPMs) fitted into a hardened 40-foot container whose dimensions are in compliance with ISO shipping containers requirements. In FY2019 HolosGen LLC was awarded by the Department of Energy Advanced Research Project Agency-Energy (DOE ARPA-E) under the MEITNER funding program. As part of the MEITNER award, the Argonne National Laboratory (ANL) contributed expertise through two specialized teams: The “Design Team” and the “Resource Team”. The Design Team was dedicated to validate feasibility of the Holos-Quad core and to optimize its core design through neutronics analyses. The Resource Team was dedicated to feasibility verification via high-fidelity codes of Holos-Quad thermal-hydraulic, heat transfer, shielding, and structural aspects. This report summarizes the activities conducted by ANL Design Team. A rigorous design approach based on multi-criteria optimization and code-to-code comparison involving stochastic and high-fidelity deterministic solutions was developed and employed at several evolutionary stages of the Holos-Quad design. Several generations of the Holos-Quad core were designed within this project before converging to the current full-scale Gen 2+ design that is detailed in this report. Figure EA-1 illustrates a cross-sectional view of Gen 2+ Holos-Quad core configuration, and Figure EA-2 provides a simplified perspective view of 1-of-4 SPMs. The Holos-Quad uses four thermal-hydraulically independent SPMs locked into stationary positions during power operation, surrounded by BeO reflector and structural component fully comprised within the dimensional constraints represented by traditional ISO containers. One of the benefits of this approach is to enable transportation of each SPM promptly after irradiation in shielded containers. The core is designed to operate for approximately 8 full-power years while the reactivity controls and power conversion system enable load-following operations. The reactivity controls are represented by independent, diversified, and redundant reactivity control systems based on control drums and redundant sets of shutdown rods. The high-fidelity simulation tools were used to assess detailed power and flux distributions of the three-dimensional full-core or quarter-core of the Gen 2+ configuration. Single-physics and multi-physics simulations of the neutronics code PROTEUS and the thermal-hydraulic code System Analysis Module (SAM) were performed to analyze the Holos design configurations with detailed high-fidelity solutions. The design work performed confirmed feasibility of the Holos-Quad concept, provided realistic design description for detailed design of the operational system, and identified several core design improvements to be further considered for future reactor development activities.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Avoiding Excess Computation in Asynchronous Evolutionary Algorithms

Asynchronous evolutionary algorithms are becoming increasingly popular as a means of making full use of many processors while solving computationally expensive search and optimization problems. These algorithms excel at keeping large clusters fully utilized, but may sometimes inefficiently sample an excess of fast-evaluating solutions at the expense of higher-quality, slow-evaluating ones. We introduce a steady-state parent selection strategy, SWEET (“Selection whilE EvaluaTing”), that sometimes selects individuals that are still being evaluated and allows them to reproduce early. This gives slow-evaluating individuals that have higher fitnesses an increased ability to multiply in the population. We find that SWEET appears effective in simulated take-over time analysis, but that its benefit is confined mostly to early in the run, and our preliminary study on an autonomous vehicle controller problem that involves tuning a spiking neural network proves inconclusive.

Scott, Eric↗
DOE OSTI2021

Evolutionary NAS in Light of Model Stability for Accurate Continual Learning

Continual learning, the capability to learn new knowledge from streaming data without forgetting the previous knowledge, is a critical requirement for dynamic learning systems, especially for emerging edge devices such as self-driving cars and drones. However, continual learning is still facing the catastrophic forgetting problem. Previous work illustrate that model performance on continual learning is not only related to the learning algorithms but also strongly dependent on the inherited model, i.e., the model where continual learning starts. The better stability of the inherited model, the less catastrophic forgetting and thus, the inherited model should be elaborately selected. Inspired by this finding, we develop an evolutionary neural architecture search (ENAS) algorithm that emphasizes the Stability of the inherited model, namely ENAS-S. ENAS-S aims to find optimal architectures for accurate continual learning on edge devices. On CIFAR-10 and CIFAR-100, we present that ENAS-S achieves competitive architectures with lower catastrophic forgetting and smaller model size when learning from a data stream, as compared with handcrafted DNNs.

Du, Xiaocong↗
DOE OSTI2025

Building wet planets through high-pressure magma–hydrogen reactions

Close-in transiting sub-Neptunes are abundant in our Galaxy. Planetary interior models based on their observed radius–mass relationship suggest that sub-Neptunes contain a discernible amount of either hydrogen (dry planets) or water (wet planets) blanketing a core composed of rocks and metal. Water-rich sub-Neptunes have been believed to form farther from the star and then migrate inwards to their present orbits. Here we report experimental evidence of reactions between warm, dense hydrogen fluid and silicate melt that release silicon from the magma to form alloys and hydrides at high pressures. We found that oxygen liberated from the silicate melt reacts with hydrogen, producing an appreciable amount of water up to a few tens of weight per cent, which is much greater than previously predicted based on low-pressure ideal gas extrapolation. Consequently, these reactions can generate a spectrum of water contents in hydrogen-rich planets, with the potential to reach water-rich compositions for some sub-Neptunes, implying an evolutionary relationship between hydrogen-rich and water-rich planets. Therefore, detection of a large amount of water in exoplanet atmospheres may not be the optimal evidence for planet migration in the protoplanetary disk, calling into question the assumed link between composition and planet formation location.

Horn, H. W. [Arizona State University, Tempe, AZ (↗
DOE OSTI2022

Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models

Single neuron models are fundamental for computational modeling of the brain's neuronal networks, and understanding how ion channel dynamics mediate neural function. A challenge in defining such models is determining biophysically realistic channel distributions. Here, we present an efficient, highly parallel evolutionary algorithm for developing such models, named NeuroGPU-EA. NeuroGPU-EA uses CPUs and GPUs concurrently to simulate and evaluate neuron membrane potentials with respect to multiple stimuli. We demonstrate a logarithmic cost for scaling the stimuli used in the fitting procedure. NeuroGPU-EA outperforms the typically used CPU based evolutionary algorithm by a factor of 10 on a series of scaling benchmarks. We report observed performance bottlenecks and propose mitigation strategies. Finally, we also discuss the potential of this method for efficient simulation and evaluation of electrophysiological waveforms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Comparison of Kill Switch Toxins in Plant-Beneficial Pseudomonas fluorescens Reveals Drivers of Lethality, Stability, and Escape

Kill switches provide a biocontainment strategy in which unwanted growth of an engineered microorganism is prevented by expression of a toxin gene. A major challenge in kill switch engineering is balancing evolutionary stability with robust cell killing activity in application relevant host strains. Understanding host-specific containment dynamics and modes of failure helps to develop potent yet stable kill switches. To guide the design of robust kill switches in the agriculturally relevant strain Pseudomonas fluorescens SBW25, we present a comparison of lethality, stability, and genetic escape of eight different toxic effectors in the presence of their cognate inactivators (i.e., toxin–antitoxin modules, polymorphic exotoxin–immunity systems, restriction endonuclease–methyltransferase pair). We find that cell killing capacity and evolutionary stability are inversely correlated and dependent on the level of protection provided by the inactivator gene. Decreasing the proteolytic stability of the inactivator protein can increase cell killing capacity, but at the cost of long-term circuit stability. By comparing toxins within the same genetic context, we determine that modes of genetic escape increase with circuit complexity and are driven by toxin activity, the protective capacity of the inactivator, and the presence of mutation-prone sequences within the circuit. Here, the results of our study reveal that circuit complexity, toxin choice, inactivator stability, and DNA sequence design are powerful drivers of kill switch stability and valuable targets for optimization of biocontainment systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Retrospective analysis of biochemical limitations to photosynthesis in 49 species: C 4 crops appear still adapted to pre‐industrial atmospheric [ CO 2 ]

Abstract Leaf CO 2 uptake ( A ) in C 4 photosynthesis is limited by the maximum apparent rate of PEPc carboxylation ( V pmax ) at low intercellular [CO 2 ] ( c i ) with a sharp transition to a c i ‐saturated rate ( V max ) due to co‐limitation by ribulose‐1:5‐bisphosphate carboxylase/oxygenase (Rubisco) and regeneration of PEP. The response of A to c i has been widely used to determine these two parameters. V max and V pmax depend on different enzymes but draw on a shared pool of leaf resources, such that resource distribution is optimized, and A maximized, when V max and V pmax are co‐limiting. We collected published A / c i curves in 49 C 4 species and assessed variation in photosynthetic traits between phylogenetic groups, and as a function of atmospheric [CO 2 ]. The balance of V max ‐ V pmax varied among evolutionary lineages and C 4 subtypes. Operating A was strongly V max ‐limited, such that re‐allocation of resources from V pmax towards V max was predicted to improve A by 12% in C 4 crops. This would not require additional inputs but rather altered partitioning of existing leaf nutrients, resulting in increased water and nutrient‐use efficiency. Optimal partitioning was achieved only in plants grown at pre‐industrial atmospheric [CO 2 ], suggesting C 4 crops have not adjusted to the rapid increase in atmospheric [CO 2 ] of the past few decades.

Pignon, Charles P.↗
DOE OSTI2024

Evolutionary Game Dynamics between Distributed Energy Resources and Microgrid Operator: Balancing Act for Power Factor Improvement

This article investigates the intricate dynamics between Distributed Energy Resources (DERs) and the Microgrid Operator (MGO) within a microgrid interconnected with the main grid. Employing an evolutionary game framework, the study scrutinizes the strategic evolution of DERs’ decision-making processes in their interactions with the MGO. Modeled as an evolutionary game, these interactions encapsulate the strategies adopted by DERs, resulting in stable equilibrium strategies over time. Motivated by direct benefits linked to increased active power production, DERs strive to sell all available power, while the MGO focuses on optimizing the microgrid’s overall performance. The study assesses the microgrid’s performance in terms of its power factor, emphasizing the strategic balance DERs must achieve in their active power generation to avoid penalization. This penalization results in decreased individual utility for DERs due to the overall power factor decrease resulting from their prioritization of active power generation. Additionally, the diminished overall power factor implies a decrease in MGO utility. The individual utility of each DER is further influenced by the strategies adopted by other DERs, impacting the penalization factor. Leveraging a modified IEEE 13-node distribution microgrid consisting of three DERs, the study presents case studies encompassing both cooperative and non-cooperative evolutionary game scenarios. These case studies illuminate the intricacies of interactions and the resulting equilibrium outcomes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Learning protocols for the fast and efficient control of active matter

Exact analytic calculation shows that optimal control protocols for passive molecular systems often involve rapid variations and discontinuities. However, similar analytic baselines are not generally available for active-matter systems, because it is more difficult to treat active systems exactly. Here we use machine learning to derive efficient control protocols for active-matter systems, and find that they are characterized by sharp features similar to those seen in passive systems. We show that it is possible to learn protocols that effect fast and efficient state-to-state transformations in simulation models of active particles by encoding the protocol in the form of a neural network. We use evolutionary methods to identify protocols that take active particles from one steady state to another, as quickly as possible or with as little energy expended as possible. Our results show that protocols identified by a flexible neural-network ansatz, which allows the optimization of multiple control parameters and the emergence of sharp features, are more efficient than protocols derived recently by constrained analytical methods. Our learning scheme is straightforward to use in experiment, suggesting a way of designing protocols for the efficient manipulation of active matter in the laboratory.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Dual Enhancement of Thermostability and Activity of Xylanase through Computer-Aided Rational Design

In the realm of enzyme engineering, the dual enhancement of thermostability and activity remains a challenge. Herein, we employed a computer-aided approach integrating folding free energy calculations and evolutionary analysis to engineer Paecilomyces thermophila xylanase into a hyperthermophilic enzyme for application in the paper and pulp industry. Through the computational rational design, XynM9 with superior thermostability and enhanced activity was designed. Its optimal reaction temperature increases by 10 °C to 85 °C, its T m increases by 10 °C to 93 °C, and its half-life increases 11-fold to 5.8 h. Additionally, its catalytic efficiency improves by 57% to 3926 s –1 mM –1 . Molecular dynamics simulations revealed that XynM9 is stabilized by more hydrogen bonds and salt bridges than wild-type xylanase. The mutant’s narrower catalytic cleft enhances the substrate-binding affinity, thus improving the catalytic efficiency. In harsh conditions at 80 °C and pH 10, using XynM9 significantly reduced both hemicellulose and lignin, which makes it a good candidate for use in the paper and pulp process. Finally, our study presents an accurate and efficient strategy for the dual enhancement of enzyme properties, guiding further improvement of computational tools for protein stabilization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

On Properties of Adjoint Systems for Evolutionary PDEs

We investigate the geometric structure of adjoint systems associated with evolutionary partial differential equations at the fully continuous, semi-discrete, and fully discrete levels and the relations between these levels. We show that the adjoint system associated with an evolutionary partial differential equation has an infinite-dimensional Hamiltonian structure, which is useful for connecting the fully continuous, semi-discrete, and fully discrete levels. We subsequently address the question of discretize-then-optimize versus optimize-then-discrete for both semi-discretization and time integration, by characterizing the commutativity of discretize-then-optimize methods versus optimize-then-discretize methods uniquely in terms of an adjoint-variational quadratic conservation law. For Galerkin semi-discretizations and one-step time integration methods in particular, we explicitly construct these commuting methods by using structure-preserving discretization techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Identification and characterization of substrate- and product-selective nylon hydrolases

Enzymes can rapidly and selectively hydrolyze diverse natural and anthropogenic polymers, but few have been shown to hydrolyze synthetic polyamides. Here, in this work, we synthesized and characterized a panel of 95 enzymes from the N-terminal nucleophile hydrolase superfamily with 30%–50% pairwise amino acid identity. We found that nearly 40% of the enzymes had substantial nylon hydrolase activity, but there was no relationship between phylogeny and activity, nor any evidence of prior evolutionary selection for nylon hydrolysis. Several newly identified hydrolases showed substrate selectivity, generating up to 20-fold higher product titers with nylon-6,6 versus nylon-6. However, the yield was still less than 1%, necessitating further optimization before potential applications. Finally, we determined the crystal structure and oligomerization state of a nylon-6,6-selective hydrolase to elucidate structural factors that could affect activity and selectivity. These new enzymes provide insights into nylon hydrolase evolution and opportunities for analysis and engineering of improved hydrolases.

nylon↗
DOE OSTI2024

Synchrotron‐source micro‐x‐ray computed tomography for examining butterfly eyes

Comparative anatomy is an important tool for investigating evolutionary relationships among species, but the lack of scalable imaging tools and stains for rapidly mapping the microscale anatomies of related species poses a major impediment to using comparative anatomy approaches for identifying evolutionary adaptations. We describe a method using synchrotron source micro-x-ray computed tomography (syn-μXCT) combined with machine learning algorithms for high-throughput imaging of Lepidoptera (i.e., butterfly and moth) eyes. Our pipeline allows for imaging at rates of ~15 min/mm 3 at 600 nm 3 resolution. Image contrast is generated using standard electron microscopy labeling approaches (e.g., osmium tetroxide) that unbiasedly labels all cellular membranes in a species-independent manner thus removing any barrier to imaging any species of interest. To demonstrate the power of the method, we analyzed the 3D morphologies of butterfly crystalline cones, a part of the visual system associated with acuity and sensitivity and found significant variation within six butterfly individuals. Despite this variation, a classic measure of optimization, the ratio of interommatidial angle to resolving power of ommatidia, largely agrees with early work on eye geometry across species. We show that this method can successfully be used to determine compound eye organization and crystalline cone morphology. Our novel pipeline provides for fast, scalable visualization and analysis of eye anatomies that can be applied to any arthropod species, enabling new questions about evolutionary adaptations of compound eyes and beyond.

59 BASIC BIOLOGICAL SCIENCES↗