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At least 649 records · Page 36

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NASA NTRSDate not supplied

Reflectivity Control Device (RCD) Roll Momentum Management for Solar Cruiser and Future Solar Sail Missions

Solar sails provide propulsion from solar photonic pressure without the need for propellant. This advantage can be reduced or eliminated if propellant is required for attitude control. The current state of the art for solar sail attitude control is that of the solar sail mission Near Earth Asteroid Scout, which employs an Adjustable Mass Translator for pitch and yaw momentum management of reaction wheels, but must use a cold gas Reaction Control System for roll axis momentum management. It is highly desirable for solar sails to use an attitude control system that does not include propellant, so here we present a novel method of managing momentum on the roll axis. For the upcoming Solar Cruiser solar sail mission, for roll axis momentum management we plan to use Reflectivity Control Devices (RCDs), thin-film Polyimide Liquid Crystal Devices that are capable of changing optical properties on command, thus affording the opportunity to provide “photonic only” control if placed appropriately on the surface of the sail. RCD design for roll momentum management is multifaceted, and must include performance (degree of change in optical properties), RCD array size, locations of clusters on the sail, orientation with respect to the sail plane, and orientation out of the sail plane. Here we provide an explanation of the basic technology, optimal orientation, sizing, and location of RCDs, results of trade studies for Solar Cruiser, lessons learned from Solar Cruiser, and scalability for future solar sail missions. RCDs will dramatically improve the performance of the Solar Cruiser Momentum Management System compared to that of NEA Scout and will continue to benefit other future solar sail missions.

Daniel Tyler↗

DOE OSTI2026

Towards Commercialization of Hydrophone Flow Shields to Improve Passive Acoustic Data Quality (CRADA 678) (Final Report)

This project performed testing, market research, and manufacturing refinement to ready patent-pending hydrophone flow shields developed at PNNL for commercialization. The flow shields reduce the impact of flow noise on underwater sound recordings in areas of strong flow, like tidal channels or rivers. Flow noise is a challenge in many applications where it is necessary to measure underwater sound, including measuring the sound produced by marine energy converters and monitoring marine mammals. This project addressed several key questions about flow shield performance that are high-priority for potential customers and refined existing manufacturing processes to be scalable for market-level production. Specifically, this project conducted both laboratory and field evaluations of the flow shields to comprehensively evaluate their performance. Overall, the results indicate that the flow shields reduce flow noise by over 20 dB at frequencies below 50 Hz at flow speeds greater than 0.5 m/s and are proven to survive deployments more than 6 months long.

42 ENGINEERING↗

DOE OSTI2025

In situ multi-tier auto-ignition detection applied to dual-fuel combustion simulations

Here we use an anomaly detection methodology that is centered on analyzing fourth-order joint moments (co-kurtosis), particularly focusing on its application in auto-ignition of combustion problems with large numbers of species. Unsupervised anomaly detection is challenging to generalize across problem types and domains. A recent technique, centered on analyzing information in the fourth-order joint moment co-kurtosis, has shown promise, especially for high-dimensional scientific data. In this work we present developments to the co-kurtosis based anomaly detection method needed to make it effective and scalable for large-scale distributed scientific data, such as those generated by massively parallel simulations. An in situ co-kurtosis algorithm is employed as the anomaly detection method for identifying ignition kernels in simulations of turbulent combustion. Here, we extend an existing methodology which identifies regions of the domain where anomalies are present, and add another tier of anomaly detection where the individual samples contributing to the anomaly are identified. We apply this algorithm on-the-fly to a variety of turbulent reacting flow problems and compare it to the widely used (but significantly more expensive) chemical explosive mode analysis (CEMA). We demonstrate the ability of the method to detect and identify the onset of low and high temperature ignition which can be used for computational steering, as chemical and combustion anomalies occur intermittently at spatio-temporal locations unknown a priori. Finally, we apply our lightweight in situ algorithm to an exascale high-fidelity simulation with a total of 2.4 Trillion degrees of freedom, performed using an adaptive mesh refinement solver. Furthermore, through a scalability analysis, we show that the relative computational cost of this in-situ anomaly detection algorithm compared to an iteration of the reacting flow solver is negligible.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Quantum simulation of excited states from parallel contracted quantum eigensolvers

Abstract Computing excited-state properties of molecules and solids is considered one of the most important near-term applications of quantum computers. While many of the current excited-state quantum algorithms differ in circuit architecture, specific exploitation of quantum advantage, or result quality, one common feature is their rooting in the Schrödinger equation. However, through contracting (or projecting) the eigenvalue equation, more efficient strategies can be designed for near-term quantum devices. Here we demonstrate that when combined with the Rayleigh–Ritz variational principle for mixed quantum states, the ground-state contracted quantum eigensolver (CQE) can be generalized to compute any number of quantum eigenstates simultaneously. We introduce two excited-state (anti-Hermitian) CQEs that perform the excited-state calculation while inheriting many of the remarkable features of the original ground-state version of the algorithm, such as its scalability. To showcase our approach, we study several model and chemical Hamiltonians and investigate the performance of different implementations.

Physics↗

NASA NTRSDate not supplied

Anti-Fog Coatings For Spacesuit Helmets

A permanent and robust anti-fog coating was developed that is applied to the interior of polycarbonate spacesuit helmets. The coating is a mechanically resilient and permanent superhydrophilic (SHPL) layer produced by a two-step process consisting of a plasma processing pre-etch followed by the deposition of an optically transparent Diamond Like Carbon (DLC) coating to achieve an anti-fog, SHPL-DLC layer with Water Contact Angle (WCA) < 10°. The all-dry vacuum process consists of an oxygen plasma etching to engineer the surface of the polycarbonate, creating a well-controlled nanotextured morphology, which is key to the superhydrophilic properties, followed by vacuum deposited Plasma Enhanced Chemical Vapor Deposition (PECVD) DLC coating with high surface energy. The process is carried out at low temperatures compatible with polycarbonate materials and the two steps are performed back-to-back without breaking vacuum between steps. The process is readily scalable and compatible with large scale production volumes. The SHPL-DLC coating was tested for > 200 hours exposure using a breath simulator with no detrimental effect on the coating performance. The coating was also tested for exposure to cleaning fluids, IPA chemical exposure, adhesion testing per ASTM D3359, and abrasion resistance per MIL-E-12397 with no detrimental effects.

Spacesuit↗

DOE OSTI2025

Development of Innovative Non-Concentrated Alloy (NCA) Fuel Cladding for Advanced Nuclear Reactors

As the nuclear energy sector advances toward next-generation reactors, the need for high-performance fuel cladding materials has become increasingly urgent. Traditional alloys like zirconium and stainless steel are reaching their performance limits under higher temperatures, more corrosive coolants, and extended irradiation. This report presents the development of a new class of fuel cladding materials based on Non-Concentrated Alloys (NCAs) ? multi-element systems designed to deliver enhanced mechanical strength, corrosion resistance, and radiation tolerance. Through a combination of computational modeling (Computer Coupling of Phase Diagrams and Thermochemistry: CALPHAD), simulation-guided alloy selection, and experimental fabrication via arc melting and spark plasma sintering (SPS), three strategic alloy design paths were explored: (1) FeCrAl-based NCAs, (2) refractory-lean neutron-efficient alloys, and (3) equimolar high-entropy compositions. Microstructural analysis confirmed the formation of stable body-centered cubic BCC_A2 phases, while mechanical testing demonstrated hardness values significantly exceeding those of conventional cladding materials. The results highlight the tunability of NCA systems and their potential for balancing strength and ductility ? a critical consideration for in-reactor performance. Looking forward, future work will focus on thermomechanical optimization, CALPHAD refinement, and benchmarking against industry standards to enable scalable deployment. This work not only advances the science of nuclear materials but also supports broader goals in nuclear safety, performance, and nuclear energy innovation.

36 - MATERIALS SCIENCE↗

DOE OSTI2022

Implement and Test 3D Mortar Contact in BISON

We leverage the extension of the generation of mortar segment meshes to three dimensions in MOOSE’s framework to extend thermomechanical modeling capabilities to problems with three dimensions. A modular approach to gap heat transfer physics using the mortar finite element method was created and documented, mechanical contact was extended to three dimensions—including frictional behavior, performance and ease of use were improved, and steps towards scalability of solid mechanics problems involving contact were taken. Many of these new developments are demonstrated in the simulation of 3D light-water reactor (LWR) problems, where the thermomechanical interface problem is solved using the mortar finite element method. Usage of the mortar framework has improved convergence in 2D problems and has enabled employing friction in 3D problems, of which we show results of a short, local stack of 3D pellets. Consequently, the benefits of mortar in terms of solution convergence and quality are extended to three dimensions. Section 2 discusses fundamental developments that enabled the simulation of practical mortar problems in three dimensions and other general improvements, including the reduction of the derivative container size, the modification of dual basis computations when edge dropping (lack of secondary element projection) takes place, the improvement of conditioning when employing the VCP in-edge dropping conditions, and code usability and quality improvements. These latter code enhancements include the migration of tests using “old” mortar contact constraints to using dual mortar with a semi-smooth Newton solution strategy and the reuse of lower dimensional domains for straightforwardly setting up a mortar thermomechanical LWR problem, i.e. the MOOSE action is employed for mechanical contact and the thermal LWR action is employed to capture the gas conductance, contact, and radiation components of gap heat transfer physics. Independently of the mortar LWR thermal action, we developed a modular approach to gap heat transfer that resides in MOOSE and can be leveraged, e.g., in metallic fuel problems. This approach, whose code design based on MOOSE’s user objects to model specific physics was proposed by the maintenance activity, is detailed in Section 3. Based on the dual mortar finite element method, the frictional contact constraints were extended to three dimensions. A block sheared in two directions in and out of contact with a rigid plane is employed in Section 4 to show the way the approach handles changes in frictional states (e.g. stick to slip) within a competitive number of Newton iterations. Equations and numerical results on the use of the VCP with Cartesian Lagrange multipliers, whose combination enables their direct condensation, are described in Section 5.3. Two-dimensional and three-dimensional BISON LWR simulations are discussed in Section 6. Particularly, a stack of five eccentric pellets with a surface defect is simulated and the effect of pellet-cladding friction is assessed. Finally, conclusions are outlined in Section 7.

42 ENGINEERING↗

DOE OSTI2025

Scalable freeform optimization of wide-aperture 3D metalenses by zoned discrete axisymmetry

We introduce a novel framework for design and optimization of 3D freeform metalenses that attains nearly linear scaling of computational cost with diameter, by breaking the lens into a sequence of radial “zones” with 𝑛-fold discrete axisymmetry, where 𝑛 increases with radius. This allows vastly more design freedom than imposing continuous axisymmetry, while avoiding the compromises of the locally periodic approximation (LPA) or scalar diffraction theory. Using a GPU-accelerated finite-difference time-domain (FDTD) solver in cylindrical coordinates, we perform full-wave simulation and topology optimization within each supra-wavelength zone. We validate our approach by designing millimeter and centimeter-scale, poly-achromatic, 3D freeform metalenses which outperform the state of the art. By demonstrating the scalability and resulting optical performance enabled by our “zoned discrete axisymmetry” (ZDA) and supra-wavelength domain decomposition, we highlight the potential of our framework to advance large-scale meta-optics and next-generation photonic technologies.

Sun, Mengdi [Wesleyan University]↗

NASA NTRS2004

Job Scheduling in a Heterogeneous Grid Environment

Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. However, a number of major technical hurdles must be overcome before this potential can be realized. One problem that is critical to effective utilization of computational grids is the efficient scheduling of jobs. This work addresses this problem by describing and evaluating a grid scheduling architecture and three job migration algorithms. The architecture is scalable and does not assume control of local site resources. The job migration policies use the availability and performance of computer systems, the network bandwidth available between systems, and the volume of input and output data associated with each job. An extensive performance comparison is presented using real workloads from leading computational centers. The results, based on several key metrics, demonstrate that the performance of our distributed migration algorithms is significantly greater than that of a local scheduling framework and comparable to a non-scalable global scheduling approach.

Shan, Hong-Zhang↗

DOE OSTI2024

Transforming nanoscale VO 2 (B) into a scalable sodium-ion electrode

The intermittent nature of renewable energies requires highly reliable grid-level energy storage approaches. A critical consideration in developing this technology is the areal capacity which determines battery performance and influences the cost of battery technology. Of related importance is finding new ways of developing scalable electrodes. In recent years, three-dimensional (3D) printing of conductive scaffolds has emerged as an alternative to overcome the scalability limitations of commercial tape cast electrodes. The research carried out in the current study demonstrates a successful scalability pathway for nanoscale VO 2 (B), a desirable cathode for sodium-ion batteries which has a nano-flower morphology with a crystallite size < 20 nm. By electrodepositing VO 2 (B) onto a graphene aerogel scaffold, we were able to achieve mass loading of over 100 mg·cm −2 and still possess an areal capacity of 10 mAh·cm −2 at a current density of 5 mA·cm −2 . Moreover, after 1000 cycles, these electrodes retained 75% to 80% of their initial capacity. Even at high loading levels, the electrodeposited VO 2 (B) exhibits pseudocapacitive material signatures such as a box-like voltammetry response, linear galvanostatic response, and no phase change upon lithiation. The scalability of the VO 2 (B) electrode is demonstrated in a series of experiments which show the areal capacity to scale upon increase in both mass loading and electrode thickness, with only small changes in specific capacity. Furthermore, this study establishes that nanoscale materials can be scaled up to achieve thick electrodes without compromising their electrochemical properties.

additive manufacturing↗

DOE OSTI2025

Decentralized Distributed Proximal Policy Optimization (DD-PPO) for High Performance Computing Scheduling on Multi-User Systems

Resource allocation in High Performance Computing (HPC) environments presents a complex and multifaceted challenge for job scheduling algorithms. Beyond the efficient allocation of system resources, schedulers must account for and optimize multiple performance metrics, including job wait time and system throughput. Traditional heuristic-based scheduling algorithms increasingly struggle and lack the efficiency needed to meet the demands and address the complexity and scale of modern HPC systems. Consequently, recent research efforts have focused on leveraging advancements in Artificial Intelligence (AI) and Deep Learning (DL), particularly Reinforcement Learning (RL), to develop more adaptable and intelligent scheduling strategies. Previous RL-based scheduling approaches have explored a range of algorithms, from Deep Q-Networks (DQN) to Proximal Policy Optimization (PPO), and more recently, hybrid methods that integrate Graph Neural Networks (GNNs) with RL techniques. However, a common limitation across these methods is their reliance on relatively small datasets, with few methods being evaluated using large-scale, multi-million-job trace datasets representative of real-world HPC workloads. Moreover, existing RL schedulers face scalability issues due to centralized policy updates, which hinder training efficiency and performance when applied to large datasets. This study introduces a novel RL-based scheduler utilizing Decentralized Distributed Proximal Policy Optimization (DD-PPO) algorithm, which supports large-scale distributed training across multiple workers without requiring parameter synchronization at every step. By eliminating reliance on centralized updates to a shared policy, the DD-PPO scheduler enhances scalability, training efficiency, and sample utilization. Experimental validation using a large real-world dataset containing over 11.5 million job traces collected from petascale HPC systems over six years assesses the influence of dataset scale on training effectiveness and compares DD-PPO performance to traditional and advanced scheduling approaches. The experimental results demonstrate improved scheduling performance in comparison to both heuristic-based schedulers and existing RL-based scheduling algorithms.

AI↗

DOE OSTI2026

Synaptic Functionality and Neuromorphic Information Processing in Membrane Ion Channel Junctions

The human brain performs complex memory and computational tasks with high energy efficiency by regulating ion transport through membrane channels. These signaling mechanisms have been inspiring the development of nanofluidic memristors that emulate synaptic behavior. Here, in this study, we describe a membrane ion channel synapse (MICS), constructed from aqueous droplets linked by gramicidin A channels, that achieves neuromorphic functionality. MICS exhibits memristive ion transport with hysteretic current–voltage behavior arising from voltage-dependent channel formation and ion transport dynamics. MICS emulates a range of synaptic behaviors including associative learning. We further demonstrate its application in reservoir computing by performing handwritten digit classification and tic-tac-toe game and explore the system parameters that improve the computational performance. This droplet-based biomimetic synapse offers a potentially scalable and energy-efficient platform for next-generation neuromorphic computing systems.

Droplet interface bilayer↗

DOE OSTI2024

Multi‐Objective Optimization for Rapid Identification of Novel Compound Metals for Interconnect Applications

Abstract Interconnect materials play the critical role of routing energy and information in integrated circuits. However, established bulk conductors, such as copper, perform poorly when scaled down beyond 10 nm, limiting the scalability of logic devices. Here, a multi‐objective search is developed, combined with first‐principles calculations, to rapidly screen over 15,000 materials and discover new interconnect candidates. This approach simultaneously optimizes the bulk electronic conductivity, surface scattering time, and chemical stability using physically motivated surrogate properties accessible from materials databases. Promising local interconnects are identified that have the potential to outperform ruthenium, the current state‐of‐the‐art post‐Cu material, and also semi‐global interconnects with potentially large skin depths at the GHz operation frequency. The approach is validated on one of the identified candidates, CoPt, using both ab initio and experimental transport studies, showcasing its potential to supplant Ru and Cu for future local interconnects.

Chemistry↗

DOE OSTI2024

Flexible dynamic boundary microgrid operation considering network and load unbalances

Flexible microgrids with dynamic boundaries have recently been introduced in the literature. With the ability to reconfigure the topology of the microgrids dynamically through remotely controlled switches, flexible microgrids with dynamic boundaries can further improve the resiliency and energy efficiency of microgrids with distributed energy resources (DERs). This paper focuses on the optimal operation considering one of the predominant characteristics of microgrids and distribution systems – unbalanced networks and loads. In existing literature, balanced modeling of microgrids is more common due to its attractive simplicity. The three-phase power unbalance has not been considered as a constraint on the generation units in a microgrid. Further, negative sequence constraints have also been neglected. In this article, we propose a set of constraints that is specifically related to the capabilities of inverter interfaced resources to supply unbalanced current/power when the microgrid is islanded from the main distribution grid. We incorporate the new set of constraints into two optimization formulations leveraging two convex relaxations of the three-phase power flow equations: mixed-integer linear programming (MILP) and mixed-integer semidefinite programming (MISDP) that optimize the dispatch of controllable switches and DERs in the microgrid. The algorithms are then extended to networked microgrids with grid-forming sources. We test the algorithms on a realistic community microgrid model in Puerto Rico as well as standardized IEEE distribution test feeders. The testing results demonstrate the performance of the proposed algorithms. The MILP is fast and scalable, and the MISDP enforces the negative sequence voltage constraints.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

AutoBEM: A scalable framework for nationwide building energy simulation and retrofit evaluation in the United States

This paper presents AutoBEM, an integrated, automated framework for nationwide building energy modeling and retrofit evaluation in the United States. Unlike prior UBEM platforms that either rely primarily on representative stock sampling or operate at city scale, AutoBEM automates the generation of building-resolved, physics-based EnergyPlus/OpenStudio simulation models at national scale using GIS-derived geometry, prototype-based assumptions, and standardized scalable workflows. Leveraging the Model America dataset and high-performance computing, AutoBEM generates and simulates energy models for 122.9 million buildings, representing 97.8% of the U.S. building stock. These models are being made publicly and freely available as the Model America v1.0 (MAv1) dataset. AutoBEM supports detailed, building-level assessments of energy consumption, CO2 emissions, and post-processed anthropogenic heat emissions (AHE), and evaluates 151 energy conservation measures (ECMs) using localized utility pricing and building characteristics. In addition, AutoBEM incorporates both typical and future climate conditions through integration with Typical Meteorological Year (TMY) and Future TMY (fTMY) weather data derived from IPCC scenarios. In a case study of Phoenix, Arizona, AutoBEM identified several high-efficiency HVAC upgrades and selected envelope measures with short modeled payback periods (1.5 years) for certain building types and standards. Simulations under future climate scenarios (SSP5–RCP8.5) project an 11.3% increase in electricity use and a 32% reduction in natural gas demand by 2100, underscoring the need for climate-adaptive retrofit planning. By enabling reproducible, bottom-up, and location-specific analysis at scale, AutoBEM provides a step toward a national digital twin of the built environment and supports data-driven screening and planning for decarbonization, resilience, and energy equity.

Li, Hang [ORNL] (ORCID:0000000306001920)↗

DOE OSTI2021

Strain-Induced Nanocavitation in Block Copolymer Thin Films for High Performance Filtration Membranes

A new pore formation process was investigated for the manufacture of composite ultrafiltration membranes. Phase-separated block copolymer (BCP) thin films supported on a compliant macroporous poly(ether sulfone) (PES) support craze under tensile strain, leaving behind pores of predictable size based on the self-assembled nanoscopic domains. The high aspect ratio pores formed in this process were used to create membranes that were highly permeable (959 L/(m 2 h bar) with near complete rejection of 40 nm diameter gold nanoparticles (AuNP). By use of BCP’s inherent ability to cavitate under strain, tedious block removal steps are avoided. Membranes can thus be prepared in a simple, roll-to-roll ready, one-step process. In this initial study, BCP craze formation and filtration performance were characterized for various polymer types, molecular weights, and thicknesses. Furthermore, all these factors influenced the BCP’s thin film morphology, mechanical performance, deformation mechanism, and ultimately filtration performance. This work demonstrates a possible new path toward achieving scalable, BCP-based ultrafiltration membranes.

36 MATERIALS SCIENCE↗

DOE OSTI2023

High-fidelity parallel entangling gates on a neutral-atom quantum computer

The ability to perform entangling quantum operations with low error rates in a scalable fashion is a central element of useful quantum information processing. Neutral-atom arrays have recently emerged as a promising quantum computing platform, featuring coherent control over hundreds of qubits and any-to-any gate connectivity in a flexible, dynamically reconfigurable architecture. The main outstanding challenge has been to reduce errors in entangling operations mediated through Rydberg interactions. Here we report the realization of two-qubit entangling gates with 99.5% fidelity on up to 60 atoms in parallel, surpassing the surface-code threshold for error correction. Our method uses fast, single-pulse gates based on optimal control, atomic dark states to reduce scattering and improvements to Rydberg excitation and atom cooling. We benchmark fidelity using several methods based on repeated gate applications, characterize the physical error sources and outline future improvements. Finally, we generalize our method to design entangling gates involving a higher number of qubits, which we demonstrate by realizing low-error three-qubit gates. By enabling high-fidelity operation in a scalable, highly connected system, these advances lay the groundwork for large-scale implementation of quantum algorithms, error-corrected circuits and digital simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Electronic properties and optical spectra of donor–acceptor conjugated organic polymers

Organic semiconductors based on conjugated donor-acceptor (D–A) polymers are a unique platform for electronic, spintronic, and energy-harvesting devices. Understanding the electronic structure of D–A polymers with a small band gap is essential for developing next-generation technologies. Here, we investigate the electronic structure and optical spectra of cyclopentadithiophene-based closed/open-shell D–A polymers using density functional theory and the Bethe–Salpeter equation based on G 0 W 0 approximation. We explored the role of different acceptor units and chemical substitutions on the structural changes and, more importantly, electronic, optical, and dielectric behavior. We found that the computed first exciton peak of the polymers agreed well with the available experimentally measured optical gap. Furthermore, D–A polymers with open-shell character display higher dielectric constant than the closed-shell polymers. We show that the exceptional performance of polycyclopentadithiophene-thiophenylthiadiazoloquinoxaline (PCPDT-TTQ) as a scalable n-type material for Faradaic supercapacitors can be partly ascribed to its elevated dielectric constant. Consequently, these D–A polymers, characterized by their high dielectric constants, exhibit significant potential for various applications, including energy storage, organic electronics, and the production of dielectric films.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗