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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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NASA NTRS2002

The International Space Station as a microgravity research platform

The International Space Station will provide a "World Class" environment for microgravity research. Ensuring this environment requires care in all aspects of its design. These aspects include consideration of the acceleration at near-orbit-tune-periods, such as gravity gradients and station drag, as well as controlling station structural dynamic modes, mechanical disturbances, and crew disturbances. Station designers must also ensure that the required acceleration environment is provided for long duration. The microgravity requirements placed on ISS will be reviewed, along with major considerations for achieving such an environment. Further, a description of the Space Station program strategy and implementation for meeting those requirements will be discussed. c2002 Published by Elsevier Science Ltd.

Spacecraft/instrumentation/standards↗

NASA NTRS1990

Spaceport aurora: An orbiting transportation node

With recent announcements of the development of permanently staffed facilities on the Moon and Mars, the national space plan is in need of an infrastructure system for transportation and maintenance. A project team at the University of Houston College of Architecture and the Sasakawa International Center for Space Architecture, recently examined components for a low Earth orbit (LEO) transportation node that supports a lunar build-up scenario. Areas of investigation included identifying transportation node functions, identifying existing space systems and subsystems, analyzing variable orbits, determining logistics strategies for maintenance, and investigating assured crew return systems. The information resulted in a requirements definition document, from which the team then addressed conceptual designs for a LEO transportation node. The primary design drivers included: orbital stability, maximizing human performance and safety, vehicle maintainability, and modularity within existing space infrastructure. For orbital stability, the power tower configuration provides a gravity gradient stabilized facility and serves as the backbone for the various facility components. To maximize human performance, human comfort is stressed through zoning of living and working activities, maintaining a consistent local vertical orientation, providing crew interaction and viewing areas and providing crew return vehicles. Vehicle maintainability is accomplished through dual hangars, dual work cupolas, work modules, telerobotics and a fuel depot. Modularity is incorporated using Space Station Freedom module diameter, Space Station Freedom standard racks, and interchangeable interior partitions. It is intended that the final design be flexible and adaptable to provide a facility prototype that can service multiple mission profiles using modular space systems.

Source record↗

DOE OSTI2024

Genome reduction improves octanoic acid production in scale down bioreactors

Microorganisms in large-scale bioreactors are exposed to heterogeneous environmental conditions due to physical mixing constraints. Nutritional gradients can lead to transient expression of energetically wasteful stress responses and as a result, can reduce the titres, rates and yields of a bioprocess at larger scales. To what extent these process parameters are impacted is often unknown and therefore bioprocess scale-up comes with major risk. Designing platform strains to account for these intermittent stresses before introducing synthesis pathways is one strategy for de-risking bioprocess development. For example, Escherichia coli strain RM214 is a derivative of wild-type MG1655 that has had several genes and whole operons removed from its genome based on their metabolic cost. In this study, we engineered E. coli strain RM214 (referred to as WG02) to produce octanoic acid from glycerol in batch-flask and fed-batch bioreactor cultivations and compared it to an octanoic acid-producing E. coli MG1655 (WG01). In batch flask cultivations, the two strains performed similarly. However, in carbon limited fed-batch bioreactor cultivations, WG02 provided a greater than 22% boost to biomass compared to WG01 while maintaining similar titres of octanoic acid. Reducing the biomass accumulation of WG02 with nitrogen limited fed-batch cultivation resulted in a 16% improvement in octanoic acid titre over WG01. Finally, in a scale-down system consisting of a stirred tank reactor (representing a well-mixed zone) and plug flow reactor (representing an intermittent carbon starvation zone), WG02 again improved octanoic acid titre by almost 18% while maintaining similar biomass concentrations as WG01.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Oxygen Electrocatalysis on Mixed-Metal Oxides/Oxyhydroxides: From Fundamentals to Membrane Electrolyzer Technology

Catalyzing the oxygen evolution reaction (OER) is important for key energy-storage technologies, particularly water electrolysis and photoelectrolysis for hydrogen fuel production. Under neutral-to-alkaline conditions, first-row transition-metal oxides/(oxy)hydroxides are the fastest-known OER catalysts and have been the subject of intense study for the past decade. Critical to their high performance is the intentional or accidental addition of Fe to Ni/Co oxides that convert to layered (oxy)hydroxide structures during the OER. Unraveling the role that Fe plays in the catalysis and the molecular identity of the true “active site” has proved challenging, however, due to the dynamics of the host structure and absorbed Fe sites as well as the diversity of local structures in these disordered active phases. In this Account, we highlight our work to understand the role of Fe in Ni/Co (oxy)hydroxide OER catalysts. We first discuss how we characterize the intrinsic activity of the first-row transition-metal (oxy)hydroxide catalysts as thin films by accounting for the contributions of the catalyst-layer thickness (mass loading) and electrical conductivity as well as the underlying substrate’s chemical interactions with the catalyst and the presence of Fe species in the electrolyte. We show how Fe-doped Ni/Co (oxy)hydroxides restructure during catalysis, absorb/desorb Fe, and in some cases degrade or regenerate their activity during electrochemical testing. We highlight the relevant techniques and procedures that allowed us to better understand the role of Fe in activating other first-row transition metals for OER. We find several modes of Fe incorporation in Ni/Co (oxy)hydroxides and show how those modes correlate with activity and durability. We also discuss how this understanding informs the incorporation of earth-abundant transition-metal OER catalysts in anion-exchange-membrane water electrolyzers (AEMWE) that provide a locally basic anode environment but run on pure water and have advantages over the more-developed proton-exchange-membrane water electrolyzers (PEMWE) that use platinum-group-metal (PGM) catalysts. We outline the key issues of introducing Fe-doped Ni/Co (oxy)hydroxide catalysts at the anode of the AEMWE, such as the oxidative processes triggered by Fe species traveling through the polymer membrane, pH-gradient effects on the catalyst stability, and possibly limited catalyst utilization in the compressed stack configuration. We also suggest possible mitigation strategies for these issues. Lastly, we summarize remaining challenges including the long-term stability of Fe-doped Ni/Co (oxy)hydroxides under OER conditions and the lack of accurate models of the dynamic active surface that hinder our understanding of, and thus ability to design, these catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1997

Three-Dimensional Viscous Alternating Direction Implicit Algorithm and Strategies for Shape Optimization

A gradient-based shape optimization based on quasi-analytical sensitivities has been extended for practical three-dimensional aerodynamic applications. The flow analysis has been rendered by a fully implicit, finite-volume formulation of the Euler and Thin-Layer Navier-Stokes (TLNS) equations. Initially, the viscous laminar flow analysis for a wing has been compared with an independent computational fluid dynamics (CFD) code which has been extensively validated. The new procedure has been demonstrated in the design of a cranked arrow wing at Mach 2.4 with coarse- and fine-grid based computations performed with Euler and TLNS equations. The influence of the initial constraints on the geometry and aerodynamics of the optimized shape has been explored. Various final shapes generated for an identical initial problem formulation but with different optimization path options (coarse or fine grid, Euler or TLNS), have been aerodynamically evaluated via a common fine-grid TLNS-based analysis. The initial constraint conditions show significant bearing on the optimization results. Also, the results demonstrate that to produce an aerodynamically efficient design, it is imperative to include the viscous physics in the optimization procedure with the proper resolution. Based upon the present results, to better utilize the scarce computational resources, it is recommended that, a number of viscous coarse grid cases using either a preconditioned bi-conjugate gradient (PbCG) or an alternating-direction-implicit (ADI) method, should initially be employed to improve the optimization problem definition, the design space and initial shape. Optimized shapes should subsequently be analyzed using a high fidelity (viscous with fine-grid resolution) flow analysis to evaluate their true performance potential. Finally, a viscous fine-grid-based shape optimization should be conducted, using an ADI method, to accurately obtain the final optimized shape.

Pandya, Mohagna J.↗

DOE OSTI2026

Protein Adhesion on Semi-Fluorinated Polystyrene Surfaces in Static and Dynamic Measurements

Reducing protein adhesion is a critical strategy in fouling-resistant material innovation, with broad applications spanning biomedical and healthcare devices, biosensors, industrial and environmental systems, and other important technological domains. Here, in this study, we elucidated protein adhesion behavior on polystyrene-based thin films by neutron reflectometry (NR) and quartz crystal microbalance with dissipation (QCM-D), using both lysozyme and bovine serum albumin (BSA) as model proteins. To this end, semifluorinated polystyrene thin films with gradient wettability and surface energy were fabricated through dry processing using plasma oxidation and gas-phase deposition. Although it is believed that a fully fluorinated alkyl chain offers extremely low surface energy, thus rejecting foulants, and has been used in many fouling-resistant surface designs, enhanced protein–surface interactions were observed consistently in NR and QCM-D results, due to the combined effects of surface morphology and chemistry. On the contrary, depositing shorter fluorinated silane onto a hydrophilic PS surface contributed to a more homogeneous nanoscale fluorine coating, resulting in less initial protein adsorption and improved surface recovery. Comparative analysis of proteins with different sizes on the nanopatterned semifluorinated surface revealed the influence of molecular characteristics on surface interactions. Lysozyme, being smaller and more compact, showed faster adsorption kinetics and higher surface coverage but largely reversible binding, whereas BSA, with its larger and more flexible structure, formed broader and more stable interfacial layers. This study fills the gap in understanding protein adhesion within the range of hydrophobicity (water contact angle ∼90°), as current strategies often associate with extreme hydrophilic and superhydrophobic surfaces due to hydration or low-surface-energy rejection mechanisms, respectively. It also provides in-depth insights into current combinatorial fouling-resistant surface design.

Yuan, Yue [Oak Ridge National Laboratory (ORNL), O↗

DOE OSTI2023

Simulation toolkit for digital material characterization of large image-based microstructures

In this paper, an efficient image-based simulation toolkit for material characterization is presented, which is scalable to work from personal computers to workstations. The effective thermal conductivity, elasticity, and permeability are evaluated employing a computational homogenization framework based on the Finite Element Method (FEM). Two complementary open-source packages are presented: one developed in Python, which can convert digital images into voxel meshes (pyTomoviewer); the other developed in Julia, that can run numerical simulations to compute effective material properties (chpack). Also, a CUDA C version of chpack is provided (chfem_gpu). They were designed to deal with large multi-phase models, so strategies were devised to minimize their memory footprint, while avoiding a high toll on execution time. The voxel-based approach significantly simplifies the FEM meshes and allows efficient matrix-free implementations. In that sense, to handle large linear systems of equations, the element-by-element (EBE) technique is adopted, in conjunction with a low-memory implementation of the Preconditioned Conjugate Gradient (PCG) method. Finally, the code was thoroughly tested on an artificial geometry made of a square array of cylinders, for which analytical solutions exist, as well as on a real micro-tomographic reconstruction of FiberForm TM , a carbon preform commonly used in thermal protection systems.

36 MATERIALS SCIENCE↗

DOE OSTI2026

torch-einshard v1.0

torch-einshard is a Python library for describing local and distributed PyTorch tensor computations with compact, einsum-like notation. Its expressions name logical axes, specify how they are sharded across a PyTorch DeviceMesh, and represent partial reductions. The library automatically performs contractions, permutations, reshaping, splitting, gathering, reduction, reduce-scatter, and repartitioning while preserving autograd. Additional features include sharding-aware FFTs, tensor rolls, halo exchange, sliding windows, 1D–3D convolutions, uneven-shard handling, parameter initialization and gradient management, and cost-based execution planning. It is designed for scientific machine learning and large-model workloads, including tensor-, sequence-, and spatial-parallel MLPs, attention, convolutions, and spectral operations. Compared with manually combining torch.einsum and distributed collectives, torch-einshard expresses both the mathematical operation and data placement in one readable formula. This reduces boilerplate and synchronization errors, keeps forward and backward communication consistent, and allows the library to select optimized collective strategies without changing model code.

Morozov, Dmitriy [Lawrence Berkeley National Labor↗

DOE OSTI2020

A Nonlocal-Gradient Descent Method for Inverse Design in Nanophotonics

Local-gradient-based optimization approaches lack nonlocal exploration abilityrequired for escaping from local minima when searching non-convex landscapes.A directional Gaussian smoothing (DGS) approach was recently proposed in [29]and used to define a truly nonlocal gradient, referred to as the DGS gradient, inorder to enable nonlocal exploration in high-dimensional black-box optimization.Promising results show that replacing the traditional local gradient with the nonlocalDGS gradient can significantly improve the performance of gradient-based methodsin optimizing highly multi-modal loss functions. However, the current DGS methodis designed for unbounded and uncontrained optimization problems, making itinapplicable to real-world engineering optimization problems where the tuningparameters are often bounded and the loss function is usually constrained byphysical processes. In this work, we propose to extend to the DGS approachto the constrained inverse design framework in order to find better optima ofmulti-modal loss functions. A series of adaptive strategies for smoothing radiusand learning rate updating are developed to improve the computational efficiencyand robustness. Our methodology is demonstrated by an example of designing ananoscale wavelength demultiplexer, and shows superior performance compared tothe state-of-the-art approaches. By incorporating volume constraints, the optimizeddesign achieves an equivalently high performance but significantly reduces theamount of material usage.

Bi, Sirui↗

DOE OSTI2022

Tapered helical undulator system for high efficiency energy extraction from a high brightness electron beam

In this work we discuss the design choices and construction strategy of the tapered undulator system designed for a high energy extraction efficiency experiment in the ultraviolet region of the electromagnetic spectrum planned for installation at the Argonne National Laboratory Linac Extension Area (LEA) beamline. The undulator is comprised of 4 sections pure permanent magnet Halbach array separated by short break sections, each one of them housing a focusing quadrupole doublet and a phase shifter. The quadrupoles use a novel hybrid design which allows one to vary the gradient and match the beam transversely. Finally, the undulator tapering profile is optimized to maximize the energy conversion efficiency from a 340 MeV 1 kA beam into coherent 257.5 nm radiation taking into account the longitudinal current profile generated by the linac.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2000

Neural Network and Response Surface Methodology for Rocket Engine Component Optimization

The goal of this work is to compare the performance of response surface methodology (RSM) and two types of neural networks (NN) to aid preliminary design of two rocket engine components. A data set of 45 training points and 20 test points obtained from a semi-empirical model based on three design variables is used for a shear coaxial injector element. Data for supersonic turbine design is based on six design variables, 76 training, data and 18 test data obtained from simplified aerodynamic analysis. Several RS and NN are first constructed using the training data. The test data are then employed to select the best RS or NN. Quadratic and cubic response surfaces. radial basis neural network (RBNN) and back-propagation neural network (BPNN) are compared. Two-layered RBNN are generated using two different training algorithms, namely solverbe and solverb. A two layered BPNN is generated with Tan-Sigmoid transfer function. Various issues related to the training of the neural networks are addressed including number of neurons, error goals, spread constants and the accuracy of different models in representing the design space. A search for the optimum design is carried out using a standard gradient-based optimization algorithm over the response surfaces represented by the polynomials and trained neural networks. Usually a cubic polynominal performs better than the quadratic polynomial but exceptions have been noticed. Among the NN choices, the RBNN designed using solverb yields more consistent performance for both engine components considered. The training of RBNN is easier as it requires linear regression. This coupled with the consistency in performance promise the possibility of it being used as an optimization strategy for engineering design problems.

Vaidyanathan, Rajkumar↗

DOE OSTI2022

Chemical design of self-propelled Janus droplets

Solubilizing, self-propelling droplets have emerged as a rich chemical platform for the exploration of active matter, but isotropic droplets rely on spontaneous symmetry breaking to sustain motion. The introduction of permanent asymmetry, e.g., in the form of a biphasic Janus droplet, has not been explored as a comprehensive design strategy for active droplets, despite the widespread use of Janus structures in motile solid particles. Here, we uncover the chemomechanical framework underlying the self-propulsion of biphasic Janus oil droplets solubilizing in aqueous surfactant. We elucidate how droplet propulsion is influenced by the degree of oil mixing, droplet shape, and oil solubilization rates for a range of oil combinations. In addition, spatiotemporal control over droplet swimming speed and orientation is demonstrated through the application of thermal gradients applied via joule heating and laser illumination. Finally, we also explore the interactions between collections of Janus droplets, including the spontaneous formation of spinning multi-droplet clusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1993

Aerodynamic shape optimization using preconditioned conjugate gradient methods

In an effort to further improve upon the latest advancements made in aerodynamic shape optimization procedures, a systematic study is performed to examine several current solution methodologies as applied to various aspects of the optimization procedure. It is demonstrated that preconditioned conjugate gradient-like methodologies dramatically decrease the computational efforts required for such procedures. The design problem investigated is the shape optimization of the upper and lower surfaces of an initially symmetric (NACA-012) airfoil in inviscid transonic flow and at zero degree angle-of-attack. The complete surface shape is represented using a Bezier-Bernstein polynomial. The present optimization method then automatically obtains supercritical airfoil shapes over a variety of freestream Mach numbers. Furthermore, the best optimization strategy examined resulted in a factor of 8 decrease in computational time as well as a factor of 4 decrease in memory over the most efficient strategies in current use.

Burgreen, Greg W.↗

DOE OSTI2023

A Hydrogen Iron Flow Battery with High Current Density and Long Cyclability Enabled Through Circular Water Management

The hydrogen‐iron (HyFe) flow cell has great potential for long‐duration energy storage by capitalizing on the advantages of both electrolyzers and flow batteries. However, its operation at high current density (high power) and over continuous cycling testing has yet to be demonstrated. In this paper, we discuss our design and demonstration of a water management strategy that supports high current and long cycling performance of a HyFe flow cell. Water molecules associated with the movement of protons from the iron electrode to the hydrogen electrode are sufficient to hydrate the membrane and electrode at a low current density of 100 mA cm −2 during the charge process. At higher charge current density, more aggressive measures must be taken to counter back‐diffusion driven by the acid concentration gradient between the iron and hydrogen electrodes. Our water management approach is based on water vapor feeding in the hydrogen electrode, and water evaporation in the iron electrode, thus enabling the high current density operation of 300 mA cm −2 .

25 ENERGY STORAGE↗

DOE OSTI2023

A Hydrogen Iron Flow Battery with High Current Density and Long Cyclability Enabled Through Circular Water Management

The hydrogen‐iron (HyFe) flow cell has great potential for long‐duration energy storage by capitalizing on the advantages of both electrolyzers and flow batteries. However, its operation at high current density (high power) and over continuous cycling testing has yet to be demonstrated. In this article, we discuss our design and demonstration of a water‐management strategy that supports high current and long‐cycling performance of a HyFe flow cell. Water molecules associated with the movement of protons from the iron electrode to the hydrogen electrode are sufficient to hydrate the membrane and electrode at a low current density of 100 mA cm −2 during the charge process. At higher charge current density, more aggressive measures must be taken to counter back‐diffusion driven by the acid concentration gradient between the iron and hydrogen electrodes. Our water‐management approach is based on water vapor feeding in the hydrogen electrode and water evaporation in the iron electrode, thus enabling high current density operation of 300 mA cm −2 .

Yan, Litao↗

DOE OSTI2025

Differentiable lagrangian shock hydrodynamics with application to stable shock acceleration of density interfaces

We develop a gradient based optimization approach for the equations of compressible, Lagrangian hydrodynamics and demonstrate how it can be employed to automatically uncover strategies to control hydrodynamic instabilities arising from shock acceleration of density interfaces. Strategies for controlling the Richtmyer-Meshkov instability (RMI) are of great benefit for inertial confinement fusion (ICF) where shock interactions with many small imperfections in the density interface lead to instabilities which rapidly grow over time. These instabilities lead to mixing which, in the case of laser driven ICF, quenches the runaway fusion process ruining the potential for positive energy return. Here, we demonstrate that control of these instabilities can be achieved by optimization of initial conditions with ( > 100) parameters. Optimizing over a large parameter space like this is not possible with gradient-free optimization strategies. This requires computation of the gradient of the outputs of a numerical solution to the equations of Lagrangian hydrodynamics with respect to the inputs. We show that the efficient computation of these gradients is made possible via a judicious application of (i) adjoint methods, the exact formal representation of sensitivities involving partial differential equations, and (ii) automatic differentiation (AD), the algorithmic calculation of derivatives of functions. Careful regularization of multiple operators including artificial viscosity and timestep control is required. We perform design optimization of > 100 parameter energy field driving the Richtmyer Meshkov instability showing significant suppression while simultaneously enhancing the acceleration of the interface relative to a nominal baseline case.

Hydrophysics↗

DOE OSTI2025

Enhancing water and oxygen transport through electrode engineering for AEM water electrolyzers

Anion-exchange membrane water electrolyzers (AEMWEs) can accelerate the deployment of more efficient and affordable hydrogen production solutions. Here, electrode structure is shown to affect water back-diffusion and oxygen transport, which, in return, governs overpotential behaviors in AEMWEs. Measurements indicate that electrode with copious catalytic sites produces water close to the AEM, creating a higher water gradient and driving water back-diffusion, which improves membrane hydration and mass transport. In situ measurement reveals a high pH gradient near the anode surface, which affects anode kinetics. Operando measurement shows reduced oxygen accumulation when decoupling oxygen production and transport on anode. Catalyst ink rheology and stability are tuned with additives to realize scalable fabrication of electrodes with enhanced transport features, allowing AEMWE to operate at 2 A cm−2 for over 1,000+ h at a 2.3 μV h−1 degradation rate. Analysis during and post-durability provides insights into degradation mechanisms. This work demonstrates an electrode design strategy for efficient and durable AEMWEs.

AEM↗

DOE OSTI2022

A deep learning-enhanced framework for multiphysics joint inversion

Joint inversion has drawn considerable attention due to the availability of multiple geophysical data sets, ever-increasing computational resources, the development of advanced algorithms, and its ability to reduce inversion uncertainty. A key issue of joint inversion is to develop effective strategies to link different geophysical data in a unified mathematical framework, in which the information obtained from different models can complement each other. We have developed a deep learning-enhanced joint inversion framework to simultaneously reconstruct different physical models by fusing different types of geophysical data. Traditionally, structure similarity constraints are pursued by joint inversion algorithms using manually crafted formulations (e.g., cross gradient). The constraint is constructed by a deep neural network (DNN) during the learning process. The framework is designed to combine the DNN and a traditional independent inversion workflow and improve the joint inversion result iteratively. The network can be easily extended to incorporate multiphysics without structural changes. Numerical experiments on the joint inversion of 2D DC resistivity data and seismic traveltime are used to validate our method. In addition, this learning-based framework demonstrates excellent generalization abilities when tested on data sets using different geologic structures. It also can handle different sensing configurations and nonconforming discretization.

Geochemistry & Geophysics↗