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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Invertible Neural Networks for Aerodynamic Design of Wind Turbine Blades

The state-of-the-practice methods for aerodynamic design of wind turbine blads use linearized blade element momentum theory (BEM) to optimize the twist and chord profiles from a pre-selected set of 2D airfoil shapes. In this work, we apply invertible neural network (INN) tools to enable the rapid inverse aerodynamic design of wind turbine blades including component airfoils. The INN is trained on data obtained through the use of robust automated mesh generation and the HAMSTR computational fluid dynamics solver with advanced turbulence and transition models validated for turbine applications. Our design technique is a significant improvement over the state-of-the-practice linearized blade element momentum (BEM) techniques in capturing 3D nonlinear aerodynamic effects that are critical for optimal design of the rotors. This is made possible by developing sparse, invertible neural networks (INNs) for inverse design and optimization that realize a 100x cost reduction compared to adjoint-based computational fluid dynamics (CFD) approaches, while enabling increased robustness of the final design. We demonstrate the INN tool for design of a section of the NREL 5-MW blade. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of wind turbine blades.

aerodynamic design↗
DOE OSTI2026

Evaluating Supply Prioritization Strategies for Risk-Informed Decision Making in an Arbitrary Gas Network

Supply disruptions and infrastructure failures in natural gas networks present critical challenges to energy reliability and risk-informed planning. This study evaluates two supply prioritization strategies, Maximum Delivery Prioritization (MDP) and Demand-Based Prioritization (DBP), within an arbitrary natural gas network under conditions of supply shortage. Model performance under both strategies is assessed in response to node and edge failure using demand satisfaction metrics, system-wide and localized dependency scores, and geographic information system (GIS)-based spatial analysis. Results show that DBP better preserves supply for high-demand nodes, while MDP offers broader coverage. The underlying network topology plays a critical role in shaping prioritization outcomes. Integrated GIS visualization enhances the interpretability of vulnerability assessments, revealing structurally critical components and localized vulnerabilities. The proposed framework supports scalable, data-driven decision-making for infrastructure planners and engineers, enabling improved disruption recovery and efficiency in constrained natural gas networks. These insights contribute to the development of more robust energy systems capable of withstanding stress and disruptions.

Peterson, Steven [ORNL] (ORCID:0000000287672998)↗
DOE OSTI2020

DNA Reaction–Diffusion Attractor Patterns

Abstract Living systems can form and recover complex chemical patterns with precisely sized features in the ranges of tens or hundreds of microns. We show how designed reaction–diffusion processes can likewise produce precise patterns, termed attractor patterns, that reform their precise shape after being perturbed. We use oligonucleotide reaction networks, photolithography, and microfluidic delivery to form precisely controlled attractor patterns and study the responses of these patterns to different localized perturbations. Linear and “hill”‐shaped patterns formed and stabilized into shapes and at time scales consistent with reaction–diffusion models. When patterns were perturbed in particular locations with UV light, they reliably reformed their steady‐state profiles. Recovery also occurred after repeated perturbations. By designing the far‐from‐equilibrium dynamics of a chemical system, this study shows how it is possible to design spatial patterns of molecules that are sustained and regenerated by continually evolving towards a specific steady state configuration.

Dorsey, Phillip James↗
DOE OSTI2020

DNA Reaction–Diffusion Attractor Patterns

Living systems can form and recover complex chemical patterns with precisely sized features in the ranges of tens or hundreds of microns. We show how designed reaction-diffusion processes can likewise produce precise patterns, termed attractor patterns, that reform their precise shape after being perturbed. We use oligonucleotide reaction networks, photolithography and microfluidic delivery to form precisely controlled attractor patterns and study the responses of these patterns to different localized perturbations. Linear and ‘hill’-shaped patterns formed and stabilized into shapes and at time scales consistent with reaction-diffusion models. When patterns were perturbed in particular locations with UV light, they reliably reformed their steady state profiles. Recovery also occurred after repeated perturbations. As a result, by designing the far-from-equilibrium dynamics of a chemical system, this study shows how it is possible to design spatial patterns of molecules that are sustained and regenerated by continually evolving towards a specific steady state configuration

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1999

A Neural Network Aero Design System for Advanced Turbo-Engines

An inverse design method calculates the blade shape that produces a prescribed input pressure distribution. By controlling this input pressure distribution the aerodynamic design objectives can easily be met. Because of the intrinsic relationship between pressure distribution and airfoil physical properties, a Neural Network can be trained to choose the optimal pressure distribution that would meet a set of physical requirements. Neural network systems have been attempted in the context of direct design methods. From properties ascribed to a set of blades the neural network is trained to infer the properties of an 'interpolated' blade shape. The problem is that, especially in transonic regimes where we deal with intrinsically non linear and ill posed problems, small perturbations of the blade shape can produce very large variations of the flow parameters. It is very unlikely that, under these circumstances, a neural network will be able to find the proper solution. The unique situation in the present method is that the neural network can be trained to extract the required input pressure distribution from a database of pressure distributions while the inverse method will still compute the exact blade shape that corresponds to this 'interpolated' input pressure distribution. In other words, the interpolation process is transferred to a smoother problem, namely, finding what pressure distribution would produce the required flow conditions and, once this is done, the inverse method will compute the exact solution for this problem. The use of neural network is, in this context, highly related to the use of proper optimization techniques. The optimization is used essentially as an automation procedure to force the input pressure distributions to achieve the required aero and structural design parameters. A multilayered feed forward network with back-propagation is used to train the system for pattern association and classification.

Sanz, Jose M.↗
NASA NTRS2000

Centrifugation of Cultured Osteoblasts And Macrophages as a Model To Study How Gravity Regulates The Function of Skeletal Cells

Mechanical loading helps define the architecture of weight-bearing bone via the tightly regulated process of skeletal turnover. Turnover occurs by the concerted activity of osteoblasts, responsible for bone formation. and osteoclasts, responsible for bone resorption. Osteoclasts are specialized megakaryon macrophages, which differentiate from monocytes in response to resorption stimuli, such as reduced weight-bearing. Habitation in space dramatically alters musculoskeletal loading, which modulates both cell function and bone structure. Our long-term objective is to define the molecular and cellular mechanisms that mediate skeletal adaptations to altered gravity environments. Our experimental approach is to apply hypergravity loads by centrifugation to rodents and cultured cells. As a first step, we examined the influence of centrifugation on the structure of cancellous bone in rats to test the ability of hypergravity to change skeletal architecture. Since cancellous bone undergoes rapid turnover we expected the most dramatic structural changes to occur in the shape of trabeculae of weight-bearing, cancellous bone. To define the cellular responses to hypergravity loads, we exposed cultured osteoblasts and macrophages to centrifugation. The intraosseous and intramedullary pressures within long bones in vivo reportedly range from 12-40 mm Hg, which would correspond to 18-59 gravity (g) in our cultures. We assumed that hydrostatic pressure from the medium above the cell layer is at least one major component of the mechanical load generated by centrifuging cultured cells. and therefore we exposed the cells to 10-50g. In osteoblasts, we examined the structure of their actin and microtubule networks, production of prostaglandin E2 (PGE2), and cell survival. Analysis of the shape of the cytoskeletal networks provides evidence for the ability of centrifugation to affect cell structure, while the production of PGE2 serves as a convenient marker for mechanical stimulation. We examined cell survival, reasoning that osteoblasts might mold skeletal structure in a hypergravity environment in part by regulating apoptosis and thus the duration of osteoblast productivity. Finally, we tested the influence of centrifugation on microbial activation of a macrophage cell line (RAW264.7). In response to the appropriate hormonal stimulation, this cell line is reportedly capable of undergoing differentiation to express osteoclast markers. In addition, a component of the cell wall of gram-negative bacteria, lipopolysaccaride (LPS), stimulates the formation of osteoclasts in vivo. Thus we tested the influence on centrifugation on RAW264.7 cells stimulated with LPS to provide an index of the function of osteoclast precursors.

Globus, Ruth K.↗
DOE OSTI2022

App2Net: Moving App Functions to Network & a Case Study on Low-latency Feedback

Recent advances in programmable networks enable custom processing of data at hundreds of gigabits per second. These advances can boost the performance of many distributed applications. Yet the high-level languages used by application developers are different from the data plane programming languages (such as P4 and NPL) used by network equipment. This language barrier slows innovation. Our hourglass-shaped architectural solution aims to lower this language barrier. This enables the application developer community to leverage programmable networks for achieving better performance. In this paper we propose a JSON-based intermediate representation to bridge the gap between applications and in-network computing. We demonstrate an instance of the solution in the context of a low-latency feedback application that enables SQL-based data filtering in a P4-based programmable environment. We also present a prototype compiler to convert an intermediate representation in JSON to P4 source.

Sankaran, Ganesh↗
DOE OSTI2024

Learning model combining convolutional deep neural network with a self-attention mechanism for AC optimal power flow

Alternating current optimal power flow (OPF) analysis is critical for efficient and reliable operation of power systems. For large systems or repetitive computations, the traditional methods such as the direct and gradient methods, or non-traditional methods, such as the genetic algorithm and simulating annealing, are time-consuming and unsuitable for real-time computing. The work in this paper proposes a novel framework to obtain the optimal solution of power flow in real-time using a combination of convolutional neural networks and a self-attention mechanism. All parameters of the power networks are rearranged in an image-like shape of a multi-channel image where each channel is a two-dimensional matrix. The proposed approach is adaptive with every input size of power systems as well as frequent variations of network topologies without intervention to the framework core. The encompassment of all power system contexts in which all parameters of internal elements, generation costs, and topology information are included, contributes to the higher accuracy of inference compared to other current machine-learning-based OPF-solving methods. Besides, the proposed framework established on ubiquitous platforms is effortlessly integrated into current infrastructures of power systems, and the great efficiency along with the computation speed may serve as a critical point for practical implications, such as enabling faster decision-making during real-time operations, predicting system contingencies, and remedial actions based on an offline pre-trained model. Furthermore, this supervised learning process is applied to the dataset of four case studies of meshed power systems: the IEEE 5-bus system (IEEE-5), the IEEE 30-bus system (IEEE-30), the IEEE 39-bus system (IEEE-39), and the IEEE 57-bus system (IEEE-57) to prove the efficacy of the proposed method.

42 ENGINEERING↗
DOE OSTI2026

Optimizing Transportation Networks for E-Waste Reverse Logistics: A Multi-Modal Cost Allocation and Pricing Strategy

The exponential growth of electronic waste (e-waste) poses critical challenges for sustainable reverse logistics and transportation network optimization. This study develops a dual-channel transportation framework for e-waste logistics that integrates dynamic freight pricing, cost allocation mechanisms, and game-theoretic coordination. The model captures interactions between centralized hubs and distributed processing networks, accounting for freight rate elasticity, volume allocation, and capacity constraints. Using Stackelberg game theory and cost-sharing strategies, the framework optimizes transportation efficiency and profit distribution across logistics channels. Numerical simulations show that the dual-channel structure increases centralized hub profit by 226.8% compared to baseline single-channel operations, while boosting total transported volume by 1.2% and nearly doubling freight collector profit under cost-sharing. Scenario analyses across regional infrastructures reveal that network density, policy incentives, and logistics costs shape routing efficiency and profit allocation. These findings suggest that coordinated strategies combining dynamic pricing, targeted infrastructure investment, and strategic cost allocation are needed to design efficient, resilient, and regionally adaptable e-waste transportation systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Real-time estimation of the electron temperature profile in DIII-D by leveraging neural-network surrogate models

Control of both the magnitude and the shape of tokamak profiles will be necessary to achieve stable, high-performance plasmas. In order to reject disturbances in real time, feedback-control algorithms rely on accurate real-time knowledge of the plasma state. When diagnostics alone are insufficient, either because they are limited in number or their measurements are too noisy, observers can be used to combine diagnostic data with a response model to provide a better estimation of different plasma properties. An observer has been developed to estimate the electron temperature profile in real time using both diagnostic data from the Thomson scattering system and a model based on the electron heat transport equation describing the evolution of the electron temperature profile. Here, neural network surrogate models are leveraged to help improve the overall model prediction while staying within computation time constraints for real-time use. The observer algorithm is shown in offline tests to produce smooth profiles that are consistent with both the diagnostic data and the electron heat transport equation. When implemented into the real-time plasma control system, this observer will provide valuable information on the electron temperature profile to many potential feedback-control applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

Abstract The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.

Jin, Ling↗
DOE OSTI2022

Throughput Analytics of Cloud Networks

A network of virtual machines at cloud server sites connected over virtual IO connections is a flexible, easily deployable, and cost-effective alternative to a physical network infrastructure with dedicated servers connected over leased fiber lines. We study the throughput performance of such a cloud network by collecting measurements over Google Cloud infrastructure spanning multiple continents. To study its ideal performance and impact of packet losses, we utilize its emulation using dedicated servers and connection hardware emulation devices. We compare the measurements over the cloud network to those over its emulation on a testbed. We examine the throughput profiles of both networks as a function of the round trip time and their utilization-concavity coefficients, estimated using measurements for common TCP versions. The throughput profile's concave-convex shape and its coefficient are critical indicators of the network performance, qualitatively and quantitatively, respectively. The results indicate their overall agreement between the production cloud network and its emulation using dedicated connections, and a near optimal throughput performance of the former except for a few under-performing connections. Also, the number of parallel flows is found to be a dominant factor in optimizing the throughput across various conditions and TCP versions.

Phanekham, Derek↗
DOE OSTI2023

Range and strength of mechanical interactions of force dipoles in elastic fiber networks

Mechanical forces generated by myosin II molecular motors drive diverse cellular processes, most notably shape change, division and locomotion. These forces may be transmitted over long range through the cytoskeletal medium – a disordered, viscoelastic network of biopolymers. The resulting cell size scale force chains can in principle mediate mechanical interactions between distant actomyosin units, leading to self-organized structural order in the cell cytoskeleton. Inspired by such force transmission through elastic structures in the cytoskeleton, we consider a percolated fiber lattice network, where fibers are represented as linear elastic elements that can both bend and stretch, and the contractile activity of myosin motors is represented by force dipoles. Then, by using a variety of metrics, we show how two such contractile force dipoles interact with each other through their mutual mechanical deformations of the elastic fiber network. As a prelude to two-dipole interactions, we quantify how forces propagate through the network from a single anisotropic force dipole by analyzing clusters of nodes connected by highly strained bonds, as well as through the decay rate of strain energy with distance from a force dipole. We show that predominant fiber bending screens out force propagation, resulting in reduced and strongly network configuration-dependent dipole interactions. On the other hand, stretching-dominated networks support longer-ranged inter-dipole interactions that recapitulate the predictions of linear elasticity theory. By characterizing the differences between tensile and compressive force propagation in the fiber network, we show how inter-dipole interaction depends on the dipoles’ mutual separation and orientation. The resulting elastic interaction energy may mediate a force between multiple distant dipoles, leading to their self-organization into ordered configurations. In conclusion, this provides a potential pathway for active mechanical force-driven structural order in elastic biopolymer networks.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Invertible Neural Networks for Airfoil Design

We report the airfoil design problem, in which an engineer seeks a shape with desired performance characteristics, is fundamental to aerodynamics. Design workflows traditionally rely on iterative optimization methods using low-fidelity integral boundary-layer methods as higher-fidelity adjoint-based computational fluid dynamics methods are computationally expensive. Surrogate-based approaches can accelerate the design process but still rely on some iterative inverse design procedure. In this work, we leverage emerging invertible neural network (INN) tools to enable the rapid inverse design of airfoil shapes for wind turbines. INNs are specialized deep-learning models with well-defined inverse mappings. When trained appropriately, INN surrogate models are capable of forward prediction of aerodynamic and structural quantities for a given airfoil shape as well as inverse recovery of airfoil shapes with specified aerodynamic and structural characteristics. The INN approach offers a roughly 100 times speed-up compared to adjoint-based methods for inverse design. We demonstrate the INN tool for inverse design on three test cases of 100 airfoils each that satisfy the performance characteristics close to those of airfoils used in wind-turbine blades. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of airfoils.

17 WIND ENERGY↗
DOE OSTI2025

Frequency Control and Disturbance Containment Using Grid-Forming Embedded Storage Networks

The paper presents a distributed approach for operating a network of inverter-based energy storage resources embedded in a bulk power system. Departing from their traditional role of steady-state reserves, the storage assets in the network are utilized as frequency-responsive resources shaping system dynamics. The power electronics converter systems interfacing the storage resources are equipped with local controllers designed to respond under transient disturbances. To this end, a safety-constrained distributed control strategy is explored. The paper compares the performance of converter-interfaced grid-forming and grid-following storage networks for fast frequency control and disturbance containment/localization. Sensitivity studies are performed to study the impact of storage size, steady-state dispatch, and controller design on dynamic performance. The findings are presented through case studies with results from the IEEE test systems.

Chatterjee, Kaustav (ORCID:0000000153273860)↗
DOE OSTI2020

Hydrogen-Bonded Organic Frameworks: A Rising Class of Porous Molecular Materials

Hydrogen-bonded organic frameworks (HOFs) are a class of porous molecular materials that rely on the assembly of organic building blocks by means of hydrogen-bonding interactions to form two-dimensional (2D) and three-dimensional (3D) crystalline networks. The reversible nature of the hydrogen-bond formation endows HOFs with the attributes of solution processability and simple regeneration. High-quality single crystals of HOFs can be grown easily for unambiguous superstructure determination by single-crystal X-ray diffraction, which is crucial for the elucidation of superstructure–property relationships. During the past decade, considerable progress has been achieved in realizing stable HOFs with permanent porosities by focusing on the design of molecular building blocks in order to introduce rigidity, auxiliary [π···π] interactions, and interpenetration of their frameworks to sustain the extended networks. The applications of HOFs are far-reaching, spanning catalysis, energy, and biomedical products as well as the storage and separation of fine chemicals. In this paper, we, first of all, provide an overview of the chronological development of HOFs, starting from the seminal work by Marsh and Duchamp in 1969 on the crystal superstructure of the hydrogen-bonded networks of trimesic acid. We identify the development of novel hydrogen-bonding motifs such as diaminotriazine (DTA), the introduction of the concept of molecular tectonics, and the establishment of permanent porosity in HOFs as being some of the milestones, which incentivized the current burgeoning research endeavors on developing HOFs as multifunctional materials. This Account is focused primarily on surveying the strategies for constructing porous 3D HOFs based on organic building blocks with peripheral carboxyl groups. These strategies are presented in the following categories: (1) the polycatenation of 2D networks by trigonal building blocks to form global 3D frameworks, (2) the utilization of building blocks with 3D geometries—tetrahedral and trigonal prismatic—that are predisposed to form 3D networks, and (3) the docking by shape-fitting of geometrically labile building blocks. We emphasize how the molecular geometry of the building blocks plays an important role in modulating the superstructures of extended frameworks so as to address specific applications. Recognizing that the in silico design of HOFs is the ultimate goal of researchers in this field, we also discuss the recent advances in superstructure prediction that lead to the formation of porous supramolecular crystals and assess the complications in implementing computational methods for HOFs with complex superstructures. We hope this Account will inspire the development of new supramolecular designs and creative approaches to crystal engineering that aid and abet the assembly of multifunctional HOFs with customizable properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

De novo design of obligate ABC-type heterotrimeric proteins

The de novo design of three protein chains that associate to form a heterotrimer (but not any of the possible two-chain heterodimers) and that can drive the assembly of higher-order branching structures is an important challenge for protein design. We designed helical heterotrimers with specificity conferred by buried hydrogen bond networks and large aromatic residues to enhance shape complementary packing. We obtained ten designs for which all three chains cooperatively assembled into heterotrimers with few or no other species present. Crystal structures of a helical bundle heterotrimer and extended versions, with helical repeat proteins fused to individual subunits, showed all three chains assembling in the designed orientation. We used these heterotrimers as building blocks to construct larger cyclic oligomers, which were structurally validated by electron microscopy. Our three-way junction designs provide new routes to complex protein nanostructures and enable the scaffolding of three distinct ligands for modulation of cell signaling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Soil microbial ecology and microbiome-metabolite linkages improve understanding of ecosystem states along terrestrial-aquatic interfaces

Coastal soils are dynamic systems where unique microbial niches are shaped by the intensity and duration of flooding between the terrestrial and aquatic boundaries of the terrestrial-aquatic interface (TAI). We aimed to understand the soil microbial community (16S rRNA gene) along the TAIs of a freshwater versus estuarine region and how it relates to organic matter (OM, via Fourier Transform Ion Cyclotron Resonance Mass Spectrometry). We studied the TAI gradients along a transect from upland (forested), transition (stressed forest), to wetland at three sites in each of the Lake Erie (freshwater) and Chesapeake Bay (estuarine) regions. Microbial communities differed significantly by region, transect position, and site. Contrary to expectations, given their dynamic hydrologies, transitions represented midpoints in microbial richness and diversity. We identified a core microbiome conserved across all transect positions within a region, highlighting potential microbial functions most resilient to environmental change. Indicator taxa unique to each transect position defined specific niches shaped by soil biogeochemistry. Co-expression networks of feature-level β-nearest-taxon indices revealed positive relationships in bacterial and OM feature contributions to community assembly. Our study provides critical insights into microbial communities at the forefront of hydrological changes in coastal areas that connect the land to lakes and oceans and remain vulnerable to changing weather patterns.

coastal ecosystems↗