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Fabrication and test of polyethylene base material for heat sterilization resistance and for battery separators in silver-zinc alkaline cell
NASA Langley and the U.S. Army have jointly sponsored programs to assess the effects of realistic flight environments and ground-based exposure on advanced composite materials and structures. Composite secondary structural components were initially installed on commercial transport aircraft in 1973; secondary and primary structural components were installed on commercial helicopters in 1979; and primary structural components were installed on commercial aircraft in the mid-to-late 1980's. Service performance, maintenance characteristics, and residual strength of numerous components are reported. In addition to data on flight components, 10 year ground exposure test results on material coupons are reported. Comparison between ground and flight environmental effects for several composite material systems are also presented. Test results indicate excellent in-service performance with the composite components during the 15 year period. Good correlation between ground-based material performance and operational structural performance has been achieved.
Brief Presenter Biography (35 word limit): Bohdan Wesely is an Aerospace Engineer in the Entry Systems and Technology Division at Ames. He has worked on a variety of projects for NASA including integrated TPS (thermal protection system) flight hardware deliveries and testing services for commercial partners. Introduction: TEMPEST is a new trajectory analysis framework that is designed to fill the gap between dedicated flight mechanics tools and aerothermal and TPS sizing tools. The project started as an SJSU master’s thesis and has since evolved into a general conceptual design tool capable of studying a wide variety of entry problems. Development is ongoing in the Entry Systems and Technology Division at NASA ARC. Why TEMPEST: Space missions involving entry into a planetary atmosphere involve a series of unique requirements across multiple disciplines. Whether it is traditional entry descent and landing (EDL), or aerocapture, the vehicle must navigate to its target landing location or orbit state, and the TPS must protect the payload during entry. The design process typically involves iterative handoffs between various flight mechanics, flow solver, and material response level tools. During the early conceptual phase, a wide variety of feasible trajectories are simulated in a Monte Carlo scenario which broadly satisfy the mission or landing requirements. Next, computational fluid dynamics (CFD), direct simulation Monte Carlo (DSMC), and other flow solver analyses are performed at various key trajectory points to generate an aero-database, heating and TPS design requirements also emerge at this stage. At this point, with updated aerodynamics from the various flow solvers, trajectories can be re-run, this in turn can change the required freestream conditions for the CFD tools, and as a project progresses, these analyses converge, and uncertainty is reduced. However, there is always a “hand-off” occurring between two inherently coupled phenomena. Analysis Description: One of the goals with TEMPEST is to use a variety of first principles estimation methods coupled with an atmosphere model to predict vehicle aerothermodynamics across the entire flight regime while propagating a 3 or 6 degree of freedom (DoF) trajectory. Aerodynamics methods include modified Newtonian, Maxwell and Cercignani- Lampis-Lord (CLL) for continuum, transitional, and free molecular flow regimes. Aerothermodynamics include boundary layer and reference enthalpy methods, and Mutation++ for non-equilibrium chemistry modeling. TEMPEST is also capable of stitching multiple trajectory segments together to study mission scenarios like multi-pass aerocapture and aero-gravity assists. Most of the program is implemented in MATLAB using modern system objects, it relies on several C++ shared libraries for supporting tools like Gmsh, the Global Reference Atmospheric Model (GRAM), and Mutation++. The various first principles aerothermal estimation methods are discretized across either a structured axisymmetric panel mesh or an unstructured tri-mesh generated from an open-source tool such as Gmsh, this allows solutions on the same mesh to be compared across tools such as CB-Aero. CFD Coupling. A physics-aware, gaussian process CFD anchoring scheme is proposed to adjust the various first principles methods as a CFD database is populated. One goal for this anchoring module is to inform the project where CFD should be run. Full knowledge of the entire trajectory, atmosphere, and aerothermodynamics allows for easier identification of high sensitivity areas and uncertainty quantification. While the first principles effects are well known and proven accurate in existing tools such as CB- Aero and Cart3D, a physics aware CFD anchoring scheme increases tool credibility across a project lifecycle. Material Response Modeling. Correct TPS sizing is critical for optimizing mass for science payloads and ensuring mission success. The process typically involves a thermal analysis along the trajectory with surface heating environments as a boundary condition. Several design constraints are maximum bondline temperature and maximum recession with various margining techniques. The material response tool FIAT, developed out of NASA Ames, is currently being integrated into the TEMPEST environment. TPS recession, shape change, mass loss, and mass property alteration are all factors that can perturb an entry trajectory. For missions like Mars 2020, recession was minimal and was safely handled separately as a post process. For missions such as Jupiter Galileo with a high TPS mass fraction or asteroid entries, recession plays a major role. The proposed fully coupled scheme is to use an epoch-based approach where the trajectory integration is halted after a recession threshold, the energy balance and FIAT are solved at each panel, the mesh, aerodynamics, and mass properties are updated, and the trajectory continues. Several computational tradeoffs have been made during the development of TEMPEST to limit the cost of a single trajectory and preserve its utility as a conceptual, rapid iteration tool. Conclusion: Development of TEMPEST is ongoing and the project is still in its infancy. This talk aims to showcase its unique capabilities to support future NASA entry systems missions.
The most stringent laboratory-based experimental limits on the existence of submegaelectronvolt sterile neutrinos are currently set by decay spectroscopy of radioactive 7 Be embedded into superconducting sensors. The systematic uncertainties are dominated by the modeling of the electron shakeup and shakeoff spectra that are not based on state-of-the-art atomic theory and do not include electron correlations or relativistic effects. We have used the multiconfiguration Dirac-Fock formalism to obtain correlated wave functions ab initio and compute all single and double shake processes in the electron capture decay of atomic 7 Be . The simulations can explain some but not all of the observed spectral features, likely because the wave functions are modified by the Ta sensor material that the 7 Be is embedded into. The new models also show that the 𝐿/𝐾 electron capture ratio of 7 Be in Ta has previously been slightly underestimated revising the previous value of 0.070(7) to a new value of 0.0756(20).
Quantum approximate optimization algorithm (QAOA) has shown promise in solving combinatorial optimization problems by providing quantum speedup on near-term gate-based quantum computing systems. However, QAOA faces challenges for high-dimensional problems due to the large number of qubits required and the complexity of deep circuits, limiting its scalability for real-world applications. In this study, we present a distributed QAOA (DQAOA), which leverages distributed computing strategies to decompose a large computational workload into smaller tasks that require fewer qubits and shallower circuits than are necessary to solve the original problem. These sub-problems are processed using a combination of high-performance and quantum computing resources. The global solution is iteratively updated by aggregating sub-solutions, allowing convergence toward the optimal solution. We demonstrate that DQAOA can handle considerably large-scale optimization problems (e.g., 1000-bit problem), achieving a high solution quality and short time-to-solution, outperforming existing strategies. Furthermore, we realize DQAOA on a quantum-centric supercomputing architecture, paving the way for practical applications of gate-based quantum computers in real-world optimization tasks. To extend DQAOA’s applicability to materials science, we further develop an active learning algorithm integrated with our DQAOA (AL-DQAOA), which involves machine learning, DQAOA, and active data production in an iterative loop. We successfully optimize photonic structures using AL-DQAOA, indicating that solving real-world optimization problems using gate-based quantum computing is feasible. We expect the proposed DQAOA to be applicable to a wide range of optimization problems and AL-DQAOA to find broader applications in material design.
A human spacecraft ATCS—especially one using single-phase coolant loops exposed to cabin atmospheric conditions—requires periodic degassing and refilling to support long-duration missions of 15 to 30 years. During initial fill operations, system maintenance, gas permeation, and quick-disconnect mating or de-mating, small amounts of gas can gradually enter the coolant system over time. This can lead to degraded heat transfer performance, pump cavitation, and potentially pump vapor lock if a significant gas volume accumulates over time. Additionally, system leaks and routine fluid sampling can gradually reduce accumulator volumes to unacceptable levels, requiring periodic refills. In more severe cases, catastrophic changes in fluid composition may necessitate emergency draining, refilling, and degassing to ensure continued system functionality. These risks were identified and mitigated on the ISS ITCS through the development of a dual-membrane degasser ORU and a Fluid Servicer System ORU for coolant refilling. These systems were developed for the fully water-based ISS ITCS Coolant[1]. The Gateway space station, the first permanent human habitat in lunar orbit, uses a propylene-glycol/water coolant mixture, which has significantly different fluid properties compared to pure water. Because microgravity degassing technologies are sensitive to fluid surface tension and viscosity, existing ISS hardware is not suitable for servicing a propylene-glycol-based TCS. Unlike the ISS, the Gateway will operate in a higher-radiation environment and must meet stricter mass constraints due to its location outside of low earth orbit. This Government Furnished Equipment (GFE) flight hardware project aims to develop a lightweight, radiation-resistant Coolant Servicer System (CSS) capable of degassing and refilling Gateway’s propylene-glycol water-based IATCS.
A human spacecraft ATCS—especially one using single-phase coolant loops exposed to cabin atmospheric conditions—requires periodic degassing and refilling to support long-duration missions of 15 to 30 years. During initial fill operations, system maintenance, gas permeation, and quick-disconnect mating or de-mating, small amounts of gas can gradually enter the coolant system over time. This can lead to degraded heat transfer performance, pump cavitation, and potentially pump vapor lock if a significant gas volume accumulates over time. Additionally, system leaks and routine fluid sampling can gradually reduce accumulator volumes to unacceptable levels, requiring periodic refills. In more severe cases, catastrophic changes in fluid composition may necessitate emergency draining, refilling, and degassing to ensure continued system functionality. These risks were identified and mitigated on the ISS ITCS through the development of a dual-membrane degasser ORU and a Fluid Servicer System ORU for coolant refilling. These systems were developed for the fully water-based ISS ITCS Coolant[1]. The Gateway space station, the first permanent human habitat in lunar orbit, uses a propylene-glycol/water coolant mixture, which has significantly different fluid properties compared to pure water. Because microgravity degassing technologies are sensitive to fluid surface tension and viscosity, existing ISS hardware is not suitable for servicing a propylene-glycol-based TCS. Unlike the ISS, the Gateway will operate in a higher-radiation environment and must meet stricter mass constraints due to its location outside of low earth orbit. This Government Furnished Equipment (GFE) flight hardware project aims to develop a lightweight, radiation-resistant Coolant Servicer System (CSS) capable of degassing and refilling Gateway’s propylene-glycol water-based IATCS.
Fatigue analysis tools can vary across industries. For example, automotive engineers often use the Wöhler (S-N) approach to design for safe-life, while aerospace engineers prioritize damage tolerance and inspection intervals, relying instead on crack growth models such as Paris’ law. Although both approaches may deal with the control of cracks in similar materials, their analysis tools and material characterizations are fundamentally distinct. This divide mirrors the classic split between stress-based strength analysis and linear elastic fracture mechanics. However, modern nonlinear models that incorporate material softening, such as cohesive laws, blur this boundary and capture fracture behaviors across scales. This presentation describes the CF23 fatigue model, which uses cohesive softening to link S-N crack initiation with crack propagation rates. CF23 spans the full fatigue spectrum, from initial propagation transients to steady-state growth and threshold conditions, offering a unified framework that bridges Wöhler and Paris-based methodologies. Example applications include fatigue crack propagation transients in adhesive interfaces and skin/stiffener separation.
Charge-state model for lead/acid batteries proposed as part of effort to make equivalent of fuel gage for battery-powered vehicles. Models based on equations that approximate observable characteristics of battery electrochemistry. Uses linear equations, easier to simulate on computer, and gives smooth transitions between charge, discharge, and recuperation.
A nonporous membrane based on the polymerization of 2,3-dihydrofuran followed by crosslinking in situ was prepared. The material is compatible with rechargeable Li battery components and, when swollen with an appropriate solvent such as tetrahydrofuran, exhibits separator resistance and Li transport equivalent to Celgard.
A low-barrier, two-step reaction pathway for peroxide-based K–O chemistry is first realized without any catalysts under the inert argon atmosphere.
A typical Bayesian inference on the values of some parameters of interest q from some data D involves running a Markov Chain (MC) to sample from the posterior $p$($q$,$n$|$D$) $\propto$ $\mathcal{L}$($D$|$q$,$n$)$p$(q)$p$($n$), where n are some nuisance parameters with a separable prior. In some cases, the nuisance parameters are high-dimensional, and their prior p(n) is itself defined only by a set of samples that have been drawn from some other MC. The MC for the posterior will typically require evaluation of p(n) at arbitrary values of n, i.e., one needs to provide a density estimator over the full n space from the provided samples. But the high dimensionality of n hinders both the density estimation and the efficiency of the MC for the posterior. We describe a solution to this problem: a linear compression of the n space into a much lower-dimensional space u, which projects away directions in n space that cannot appreciably alter $\mathcal{L}$. The algorithm for doing so is a slight modification to principal components analysis, and is less restrictive on p(n) than other proposed solutions to this issue. We demonstrate this “mode projection” technique using the analysis of 2-point correlation functions of weak lensing fields and galaxy density in the Dark Energy Survey, where n is a binned representation of the redshift distribution n(z) of the galaxies.
Microstructure-informed finite element models play a key role in the carbon–carbon composite design process. Variability in manufacturing process parameters and experimental limitations introduce model parameter uncertainty. This study quantifies the effect of model parameter uncertainty on transverse tensile fracture behavior and proposes a methodology to predict the failure mode based on competing microscale damage mechanisms. Finite element simulations incorporate fiber–matrix interface debonding with cohesive zones and matrix damage with a smeared crack band approach in a unidirectional carbon–carbon composite. Results from a variance-based global sensitivity analysis identifies interfacial and matrix damage parameters as the primary source of variability in fracture behavior. Sobol’ indices indicate that matrix and cohesive zone strengths contribute 94% of the variance in the effective ultimate stress. A local analysis elucidates the relationship between these constituent strength parameters and failure mode by estimating the probability of cohesive, matrix, and mixed-mode dominated failure. Based on the results for 4000 simulations, 93% exhibit mixed-mode or interfacial dominated failure, which underscores the crucial role of fiber–matrix interface debonding in the transverse tensile failure of carbon–carbon composites. These uncertainty quantification results facilitate more efficient model calibration and provide a framework for microstructure-informed failure predictions in the face of manufacturing-induced uncertainty.
The autonomous power expert (APEX) system is being developed at Lewis Research Center to function as a fault diagnosis advisor for a space power distribution test bed. APEX is a rule-based system capable of detecting faults and isolating the probable causes. APEX also has a justification facility to provide natural language explanations about conclusions reached during fault isolation. To help maintain the health of the power distribution system, additional capabilities were added to APEX. These capabilities allow detection and isolation of incipient faults and enable the expert system to recommend actions/procedure to correct the suspected fault conditions. New capabilities for incipient fault detection consist of storage and analysis of historical data and new user interface displays. After the cause of a fault is determined, appropriate recommended actions are selected by rule-based inferencing which provides corrective/extended test procedures. Color graphics displays and improved mouse-selectable menus were also added to provide a friendlier user interface. A discussion of APEX in general and a more detailed description of the incipient detection, recommended actions, and user interface developments during the last year are presented.
Understanding and predicting ion transport in aqueous electrolytes are crucial for advanced energy storage and biophysics, and many emergent technologies yet remain elusive. Herein, we introduce a unified framework to quantitatively describe and predict electrolyte conductivity that shifts from conventional molar concentration-based metrics to a volume fraction-based approach. Through analyzing a variety of electrolyte solutions via this perspective, we observe a universal conductivity peak at a 37% volume fraction. Small-angle X-ray scattering (SAXS) and molecular dynamics (MD) simulations reveal that nanometer-scale ion clusters drive this general behavior. Moreover, key geometric features of the ion transport pathwayssuch as pore size, tortuosity, and connectivityfollow a consistent dependence with respect to the volume fraction, reinforcing the argument for the universal conductivity trend. This paradigm shift opens new avenues for designing high-performance electrolytes and provides transformative insights for advancing studies in many fields, wherein molecular aggregates dictate transport properties.
Land-atmospheric (L-A) feedbacks have historically been studied using models whose structure and parameterizations influence outcomes and insights. The representation of L-A feedbacks based on observations alone remains an ongoing challenge for understanding boundary layer development and precipitation. To address this gap, we use ground-based passive remote sensing and in-situ observations to present an analysis of the atmosphere during 103 interstorm soil moisture drydown events spanning nine warm seasons (2016–2024) in the U.S. Southern Great Plains region. By separating events based on local L-A coupling signals and characterizing the profiles of atmospheric heat and moisture to surface energy flux behavior, we investigate the physical mechanisms linking land surface processes to boundary layer development. We find that during interstorm drydowns, the atmospheric column follows a consistent pattern: moisture increases within the boundary layer, peaks near its top, and declines rapidly above, while warming occurs through the depth. Drydowns that shift toward evaporation produce stronger and deeper thermodynamic responses than cases dominated by sensible heating, which are weaker and shallower. Additionally, moisture is accumulated faster within the boundary layer during shorter drydowns, with longer drydowns representing slower, moisture-limited growth. Drydowns with wetter initial soil moisture will sustain stronger moistening within and above the boundary layer, accelerating buoyancy growth and convective potential toward the next storm. These results provide observational evidence linking surface flux evolution to boundary layer thermodynamics and offer a process-level benchmark for evaluating coupled L-A representations in models and demonstrating the influence of soil moisture on short-term weather forecasting skill.
This paper presents a techno-economic evaluation of the proposed 600 MW, 8-hour Craig – Hayden pumped storage hydropower project using the U.S. Department of Energy’s Pumped Storage Hydropower Valuation Tool. The analysis integrates plant technical characteristics, regional grid conditions, and market-based operating assumptions to quantify stacked value streams from energy arbitrage, capacity, ancillary services, transmission congestion relief, and reliability. Both price taker and price influencer frameworks are applied to examine the impact of market participation and system interactions on lifecycle economic performance using Benefit - Cost Analysis and Multi - Criteria Decision Analysis. The results show that the price taker approach provides higher revenue estimates based on exogenous price signals, while the price influencer approach captures production cost savings, renewable curtailment reduction, and market price formation, yielding more conservative but system-representative outcomes. The study demonstrates the strategic value of long-duration PSH for enhancing operational flexibility, resource adequacy, and grid reliability in a high-renewable Western Interconnection.
Develop advanced bearing materials for hydrogen gas turbines and wind turbines. Two candidate materials have been selected based on Ames lab’s recent work on high entropy alloys and shape memory alloys. These materials are designed to exhibit a balance of hardness, strength, toughness, and corrosion resistance. The hydrogen diffusion coefficient in the NiTi based shape memory alloy is two order of magnitude lower than that of the conventional case hardened bearing steel. We plan to atomize these materials into powders, additive manufacture cylindrical bearings, then test these bearing for rolling-contact fatigue, corrosion properties, and hydrogen embrittlement. We are a vertically integrated team comprises of national labs, bearing manufacturers, raw material powders makers, and aircraft gas turbine manufacturer.