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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 253 records · Page 14

Generalized Reference Targeting for Spaceflight

For spaceflight programs to achieve some of the aggressive exploration initiatives such as visiting and landing on other celestial bodies, rendezvousing with other orbiting vehicles, and ultimately returning crew safely to Earth, an assortment of targeting algorithms to compute the necessary burns to strategically maneuver a spacecraft to a variety of destinations are required. Numerous examples exist, but rather than creating and implementing multiple targeting solutions, is it possible to have a general targeting model that can accommodate a variety of applications? Originally motivated for mission design and analysis purposes, this paper outlines a generalized reference targeting algorithm for spaceflight that may also have applications in on-orbit flight software. It accommodates arbitrary flight dynamics and both impulsive and finite burns for either absolute or relative targeting applications. It also allows for an arbitrary number of targeting design parameters such as multiple discrete correction burns or finite thrust parameters to satisfy numerous combinations of targeting constraints that can have fixed or variable time epochs. Given a reference trajectory, this targeting technique provides a general framework to quickly solve an assortment of targeting problems that may be well-defined, over-determined, or under-determined while naturally producing metrics providing insight into the controllability for a given problem formulation. Due to the derivation, speed, and accuracy of the algorithm, it lends to supporting rapid linear covariance analysis and robust trajectory design applications for a variety of flight phases such as rendezvous and docking, cislunar transfer, interplanetary flight, orbit maintenance, de-orbit, and powered descent and landing.

Targeting↗

Energy Storage Data Submission Guidelines

In order to consider and understand emerging energy storage technologies, data analysis can be executed to ascertain proper operation and performance. The technical benefits of rigorous testing and data analysis are important for the customer, the planner, developer, and system operator: the end-user has a safe, reliable system that performs predictably on a macro level. The test-and-analyze approach to verifying performance of energy storage devices, equipment, and systems integration into the grid improves the understanding of the value of energy storage over time from the economic vantage point. Demonstrating the lifecycle value of energy storage begins with the data the provider supplies for analysis. After review of energy storage data received from several providers, it has become clear that some ESS data is inconsistent and incomplete - thus leading to a question of the inefficacy of the data when it comes time to analyze it. This paper will review and propose general guidelines such as sampling rates and data points that providers must supply in order for robust data analysis to take place. Consistent guidelines are the basis of the proper protocol to (a) reduce time it takes data to reach those who are providing analyses; (b) allow them to better understand the energy storage installations; and (c) provide high quality analysis of the installation. This paper intends to serve as a starting point for what data points should be provided when monitoring. As battery technologies continue to advance and the industry expands, this paper will be updated to remain current.

25 ENERGY STORAGE↗

Towards the Robust Small-Signal Stability Region of Power Systems Under Perturbations Such as Uncertain and Volatile Wind Generation

This paper addresses how to extend the concept of small-signal stability region (SSSR) to that of robust small-signal stability region (RSSSR), within which the system can remain stable even under perturbations caused by uncertain and volatile nodal injections, such as renewable generation. We first employ the structured perturbation theory to formulate the perturbations of nodal injections in state space. Both the intensity and the locations of perturbations can be taken into account. Then, we leverage the stability radius theory and structured singular value theory to define the RSSSR in parameter subspace, enabling a systematic analysis of small-signal stability of power systems under perturbations in a region-wise manner. The hyperplane-approximation method can be employed to construct a linear closed-form approximation of RSSSR boundaries. Case studies on the modified two-area system and New England 39-node system illustrate the new concept of RSSSR and its potential applications.

power system stability analysis↗

PV Fleet Performance Data Initiative Final Technical Report (FTR)

Improved analysis and reporting of photovoltaic (PV) field performance increases the certainty of owners and financiers that systems will perform as expected. Advanced module technologies (e.g., PERC, HJT, and bifacial) introduce new degradation mechanisms and performance characteristics. This project will leverage data from the ever-increasing PV fleet to develop models and understanding of the field performance of existing and new technologies. Please see our list of public reports at https://www.nrel.gov/pv/fleet-performance-data-initiative.html. Objective 1: Support the global PV industry with scalable, robust data analysis tools that reduce the uncertainty of PV system performance and loss calculation. Objective 2: Reduce perceived risk arising from degradation rate, soiling loss, and system availability by publishing detailed statistics on U.S. fleet performance. Objective 3: Highlight factors leading to system underperformance including module type, climate, mounting configuration, etc. Objective 4: Enable continued high system performance in modern PV systems, as turnover and advances in technology bring new suppliers and high-efficiency modules into the market.

14 SOLAR ENERGY↗

GeoVision Analysis: Exploration Task Force Report

The GeoVision study effort included ground-breaking, detailed research on current and future market conditions and geothermal technologies in order to forecast and quantify the electric and non-electric deployment potentials under a range of scenarios, in addition to their impacts on the Nation’s jobs, economy and environment. Coordinated by the U.S. Department of Energy’s (DOE’s) Geothermal Technologies Office (GTO), the GeoVision study development relied on the collection, modeling, and analysis of robust datasets through seven national laboratory partners, which were organized into eight technical Task Force groups. The purpose of this report is to provide a central repository for the research conducted by the Exploration Task Force. The Exploration Task Force consists of four individuals representing three national laboratories: Patrick Dobson (task lead) and Christine Doughty of Lawrence Berkeley National Laboratory, Anna Wall of National Renewable Energy Laboratory, Travis McLing of Idaho National Laboratory, and Chester Weiss of Sandia National Laboratories. As part of the GeoVision analysis, our team conducted extensive scientific and financial analyses on a number of topics related to current and future geothermal exploration methods. The GeoVision Exploration Task Force complements the drilling and resource technology investigations conducted as part of the Reservoir Maintenance and Development Task Force. The Exploration Task Force however has focused primarily on early stage R&D technologies in exploration and confirmation drilling, along with an evaluation of geothermal financing challenges and assumptions, and innovative “blue-sky” technologies. This research was used to develop geothermal resource supply curves (through the use of GETEM) for use in the ReEDS capacity expansion modeling that determines geothermal technology deployment potential. It also catalogues and explores the large array of early-stage R&D technologies with the potential to dramatically reduce exploration and geothermal development costs, forming the basis of the GeoVision Technology Improvement (TI) scenario. These modeling topics are covered in detail in Potential to Penetration task force report. Most of the research contained herein has been published in peer-reviewed papers or conference proceedings and are cited and referenced accordingly. The sections that follow provide a central repository for all of the research findings of the Exploration and Confirmation Task Force. In summary, it provides a comprehensive discussion of Engineered Geothermal Systems (EGS) and associated technology challenges, the risks and costs of conducting geothermal exploration, a review of existing government efforts to date in advancing early-stage R&D in both exploration and EGS technologies, as well as a discussion of promising and innovative technologies and implementation of blue-sky concepts that could significantly reduce costs, lower risks, and shorten the time needed to explore and develop geothermal resources of all types.

15 GEOTHERMAL ENERGY↗

NEUROSPF: A Tool For the Symbolic Analysis of Neural Networks

This paper presents NEUROSPF, a tool for the symbolic analysis of neural networks. Given a trained neural network model, the tool extracts the architecture and model parameters and translates them into a Java representation that is amenable for analysis using the Symbolic PathFinder symbolic execution tool. Notably, NEUROSPF encodes specialized peer classes for parsing the model’s parameters, thereby enabling efficient analysis. With NEUROSPF the user has the flexibility to specify either the inputs or the network internal parameters as symbolic, promoting the application of program analysis and testing approaches from software engineering to the field of machine learning. For instance, NEUROSPF can be used for coverage-based testing and test generation, finding adversarial examples and also constraint-based repair of neural networks, thus improving the reliability of neural networks and of the applications that use them.

neural networks↗

Uncertainty propagation of p-boxes using sparse polynomial chaos expansions

In modern engineering, physical processes are modelled and analysed using advanced computer simulations, such as finite element models. Furthermore, concepts of reliability analysis and robust design are becoming popular, hence, making efficient quantification and propagation of uncertainties an important aspect. In this context, a typical workflow includes the characterization of the uncertainty in the input variables. In this paper, input variables are modelled by probability-boxes (p-boxes), accounting for both aleatory and epistemic uncertainty. The propagation of p-boxes leads to p-boxes of the output of the computational model. A two-level meta-modelling approach is proposed using non-intrusive sparse polynomial chaos expansions to surrogate the exact computational model and, hence, to facilitate the uncertainty quantification analysis. The capabilities of the proposed approach are illustrated through applications using a benchmark analytical function and two realistic engineering problem settings. They show that the proposed two-level approach allows for an accurate estimation of the statistics of the response quantity of interest using a small number of evaluations of the exact computational model. This is crucial in cases where the computational costs are dominated by the runs of high-fidelity computational models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Flight-determined stability analysis of multiple-input-multiple-output control systems

Singular value analysis can give conservative stability margin results. Applying structure to the uncertainty can reduce this conservatism. This paper presents flight-determined stability margins for the X-29A lateral-directional, multiloop control system. These margins are compared with the predicted unscaled singular values and scaled structured singular values. The algorithm was further evaluated with flight data by changing the roll-rate-to-aileron-command-feedback gain by +/- 20 percent. Also presented are the minimum eigenvalues of the return difference matrix which bound the singular values. Extracting multiloop singular values from flight data and analyzing the feedback gain variations validates this technique as a measure of robustness. This analysis can be used for near-real-time flight monitoring and safety testing.

Burken, John J.↗

Flight-determined stability analysis of multiple-input-multiple-output control systems

Singular value analysis can give conservative stability margin results. Applying structure to the uncertainty can reduce this conservatism. This paper presents flight-determined stability margins for the X-29A lateral-directional, multiloop control system. These margins are compared with the predicted unscaled singular values and scaled structured singular values. The algorithm was further evaluated with flight data by changing the roll-rate-to-aileron command-feedback gain by +/- 20 percent. Minimum eigenvalues of the return difference matrix which bound the singular values are also presented. Extracting multiloop singular values from flight data and analyzing the feedback gain variations validates this technique as a measure of robustness. This analysis can be used for near-real-time flight monitoring and safety testing.

Burken, John J.↗

Optimized Trajectory Correction Burn Placement for NRHO Orbit Maintenance

NASA's future Artemis missions plan to utilize a near rectilinear halo orbit (NRHO) in the lunar vicinity to facilitate access to the lunar surface and place other critical assets to support human exploration. This exploration architecture requires a vehicle to remain in the NRHO for long periods of time ranging from several days, to weeks, to months, and even years. Consequently, periodic orbit maintenance burns become essential to ensure the spacecraft follows the desired reference trajectory in an efficient yet effective manner despite crew activity, navigation uncertainty, maneuver execution errors, disturbance accelerations, orbit insertion dispersions, and other system limitations. This work introduces a targeting algorithm that can be utilized to analyze a variety of targeting constraints and parameters that maximizes overall performance. Techniques associated with robust trajectory optimization are used to identify the optimized number and placement for NRHO trajectory correction (NTC) burns that accounts for the mission schedule, both a primary and backup navigation system, targeting strategies and burn plan configurations, vehicle venting, thruster selection, and integrated GN\&C performance. A notional scenario extracted from the NASA Artemis III mission is used to motivate and demonstrate these concepts and performance results.

Linear Covariance Analysis↗

Robust Extraction and Multi-Technique Analysis of Micrometeoroids Captured in Low Earth Orbit

The use of low-density silica aerogel as the primary capture cell technology for the NASA Discovery mission Stardust to Comet Wild-2 [1] is a strong motivation for researchers within the Meteoritics community to develop techniques to handle this material. The unique properties of silica aerogel allow dust particles to be captured at hypervelocity speeds and to remain partially intact. The same unique properties present difficulties in the preparation of particles for analysis. Using tools borrowed from microbiologists, we have developed techniques for robustly extracting captured hypervelocity dust particles and their residues from aerogel collectors[2-3]. It is important not only to refine these extraction techniques but also to develop protocols for analyzing the captured particles. Since Stardust does not return material to Earth until 2006, researchers must either analyze particles that are impacted in the laboratory using light-gasgun facilities [e.g. 41 or examine aerogel collectors that have been exposed in low-Earth orbit (LEO) [5]. While there are certainly benefits in laboratory shots, i.e. accelerating known compositions of projectiles into aerogel, the LEO capture particles offer the opportunity to investigate real particles captured under real conditions. The aerogel collectors used in this research are part of the NASA Orbital Debris Collection Experiment that was exposed on the MIR Space Station for 18 months [5]. We have developed the capability at the UCB Space Sciences Laboratory to extract tiny volumes of aerogel that completely contain each impact event, and to mount them on micromachined fixtures so that they can be analyzed with no interfering support (Fig.1). These aerogel keystones simultaneously bring the terminal particle and the particle track to within 10 m (15 g cm- ) of the nearest aerogel surface. The extracted aerogel wedges containing both the impact tracks and the captured particles have been characterized using the synchrotron total external reflection X-ray fluorescence (TXRF) microprobe at SSRL, the Nuclear Microprobe at LLNL, synchrotron infrared microscopy at the ALS facility at LBL and the NSLS at BNL, and the Total Reflection X-ray Fluorescence (TXRF) facility at SLAC.

Westphal, A. J.↗

Robust control of systems with real parameter uncertainty and unmodelled dynamics

During this research period we have made significant progress in the four proposed areas: (1) design of robust controllers via H infinity optimization; (2) design of robust controllers via mixed H2/H infinity optimization; (3) M-delta structure and robust stability analysis for structured uncertainties; and (4) a study on controllability and observability of perturbed plant. It is well known now that the two-Riccati-equation solution to the H infinity control problem can be used to characterize all possible stabilizing optimal or suboptimal H infinity controllers if the optimal H infinity norm or gamma, an upper bound of a suboptimal H infinity norm, is given. In this research, we discovered some useful properties of these H infinity Riccati solutions. Among them, the most prominent one is that the spectral radius of the product of these two Riccati solutions is a continuous, nonincreasing, convex function of gamma in the domain of interest. Based on these properties, quadratically convergent algorithms are developed to compute the optimal H infinity norm. We also set up a detailed procedure for applying the H infinity theory to robust control systems design. The desire to design controllers with H infinity robustness but H(exp 2) performance has recently resulted in mixed H(exp 2) and H infinity control problem formulation. The mixed H(exp 2)/H infinity problem have drawn the attention of many investigators. However, solution is only available for special cases of this problem. We formulated a relatively realistic control problem with H(exp 2) performance index and H infinity robustness constraint into a more general mixed H(exp 2)/H infinity problem. No optimal solution yet is available for this more general mixed H(exp 2)/H infinity problem. Although the optimal solution for this mixed H(exp 2)/H infinity control has not yet been found, we proposed a design approach which can be used through proper choice of the available design parameters to influence both robustness and performance. For a large class of linear time-invariant systems with real parametric perturbations, the coefficient vector of the characteristic polynomial is a multilinear function of the real parameter vector. Based on this multilinear mapping relationship together with the recent developments for polytopic polynomials and parameter domain partition technique, we proposed an iterative algorithm for coupling the real structured singular value.

Chang, Bor-Chin↗

The DESI N -body Simulation Project – II. Suppressing sample variance with fast simulations

ABSTRACT Dark Energy Spectroscopic Instrument (DESI) will construct a large and precise three-dimensional map of our Universe. The survey effective volume reaches $\sim 20\, h^{-3}\, \mathrm{Gpc}^{3}$. It is a great challenge to prepare high-resolution simulations with a much larger volume for validating the DESI analysis pipelines. AbacusSummit is a suite of high-resolution dark-matter-only simulations designed for this purpose, with $200\, h^{-3}\, \mathrm{Gpc}^{3}$ (10 times DESI volume) for the base cosmology. However, further efforts need to be done to provide a more precise analysis of the data and to cover also other cosmologies. Recently, the CARPool method was proposed to use paired accurate and approximate simulations to achieve high statistical precision with a limited number of high-resolution simulations. Relying on this technique, we propose to use fast quasi-N-body solvers combined with accurate simulations to produce accurate summary statistics. This enables us to obtain 100 times smaller variance than the expected DESI statistical variance at the scales we are interested in, e.g. $k \lt 0.3\, h\, \mathrm{Mpc}^{-1}$ for the halo power spectrum. In addition, it can significantly suppress the sample variance of the halo bispectrum. We further generalize the method for other cosmologies with only one realization in AbacusSummit suite to extend the effective volume ∼20 times. In summary, our proposed strategy of combining high-fidelity simulations with fast approximate gravity solvers and a series of variance suppression techniques sets the path for a robust cosmological analysis of galaxy survey data.

cosmology: theory↗

Optimizing aluminum oxide passivation layers—Laser-induced plasmas for layer formation, real-time diagnostics, and layer evaluation

Molten salts have beneficial thermophysical and electrolytic properties, but the aggressive nature of molten salts requires corrosion mitigation strategies, such as structural material surface treatment. In this study, a laser-induced breakdown spectroscopy (LIBS) system was used to simultaneously ablate and monitor de-excitation spectra to form and identify dense alumina passivation layers. Additionally, high-frequency (kHz) LIBS imaging allowed rapid elemental mapping and depth profiling for surface O/Al ratios via minimally destructive analysis. The paired t-test rejected the null hypothesis that air and Ar cover gases were equivalent (p = 0.00012), and the analysis of variance (ANOVA) showed that overlap between shots (p = 0.03112) and cover gas flow rate (p = 0.02729) were statistically significant. At 75% overlap, the O/Al ratio increased to 10% ± 2%, and 90% overlap rose further to 31% ± 2%. Regardless of overlap, tuning the gas flow from 0.5 to 2.5 L min −1 enhanced the O/Al ratio by 24% ± 4%. LIBS depth profiling showed a 3× increase in layer thickness from 75% to 90% overlap. Molecular band peaks of AlO, a precursor to Al 2 O 3 found during treatment, suggest that band intensity could be used for real-time Al 2 O 3 optimization. Ultimately, by adjusting laser spot overlap and cover gas flow rate, a 266 nm laser was shown to form Al 2 O 3 layers on an Al6061 sample, with cover gas flow rate primarily affecting the O/Al ratio and layer thickness correlating with laser spot overlap. This combination of molecular spectra collection, imaging, and depth profiling demonstrated the robust layer analysis capabilities of LIBS.

Corrosion↗

Chemical Analysis of Fluorobenzenes via Multinuclear Detection in the Strong Heteronuclear J-Coupling Regime

Chemical analysis via nuclear magnetic resonance (NMR) spectroscopy using permanent magnets, rather than superconducting magnets, is a rapidly developing field. Performing the NMR measurement in the strong heteronuclear J-coupling regime has shown considerable promise for the chemical analysis of small molecules. Typically, the condition for the strong heteronuclear J-coupling regime is satisfied at µT magnetic field strengths and enables high resolution J-coupled spectra (JCS) to be acquired. However, the JCS response to systematic chemical structural changes has largely not been investigated. In this report, we investigate the JCS of C6H6−xFx (x = 0, 1, 2, …, 6) fluorobenzene compounds via simultaneous excitation and detection of 19F and 1H at 51.5 µT. The results demonstrate that JCS are quantitative, and the common NMR observables, including Larmor frequency, heteronuclear and homonuclear J-couplings, relative signs of the J-coupling, chemical shift, and relaxation, are all measurable and are differentiable between molecules at low magnetic fields. The results, corroborated by ab initio calculations, provide new insights into the impact of chemical structure and their corresponding spin systems on JCS. In several instances, the JCS provided more chemical information than traditional high field NMR, demonstrating that JCS can be used for robust chemical analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The Structure of Ferroselite, FeSe 2 , at Pressures up to 46 GPa and Temperatures down to 50 K: A Single-Crystal Micro-Diffraction Analysis

We conducted an in situ crystal structure analysis of ferroselite at non-ambient conditions. The aim is to provide a solid ground to further the understanding of the properties of this material in a broad range of conditions. Ferroselite, marcasite-type FeSe 2 , was studied under high pressures up to 46 GPa and low temperatures, down to 50 K using single-crystal microdiffraction techniques. High pressures and low temperatures were generated using a diamond anvil cell and a cryostat respectively. We found no evidences of structural instability in the explored P-T space. The deformation of the orthorhombic lattice is slightly anisotropic. As expected, the compressibility of the Se-Se dumbbell, the longer bond in the structure, is larger than that of the Fe-Se bonds. There are two octahedral Fe-Se bonds, the short bond, with multiplicity two, is slightly more compressible than the long bond, with multiplicity four; as a consequence the octahedral tetragonal compression slightly increases under pressure. We also achieved a robust structural analysis of ferroselite at low temperature in the diamond anvil cell. Structural changes upon temperature decrease are small but qualitatively similar to those produced by pressure.

Lavina, Barbara↗

Role of reinforcement learning for risk-based robust control of cyber-physical energy systems

Critical infrastructures such as cyber-physical energy systems (CPS-E) integrate information flow and physical operations that are vulnerable to natural and targeted failures. Safe, secure, and reliable operation and control of CPS-E is critical to ensure societal well-being and economic prosperity. Automated control is key for real-time operations and may be mathematically cast as a sequential decision-making problem under uncertainty. Emergence of data-driven techniques for decision making under uncertainty, such as reinforcement learning (RL), have led to promising advances for addressing sequential decision-making problems for risk-based robust CPS-E control. However, existing research challenges include understanding the applicability of RL methods across diverse CPS-E applications, addressing the effect of risk preferences across multiple RL methods, and development of open-source domain-aware simulation environments for RL experimentation within a CPS-E context. This article systematically analyzes the applicability of four types of RL methods (model-free, model-based, hybrid model-free and model-based, and hierarchical) for risk-based robust CPS-E control. Problem features and solution stability for the RL methods are also discussed. We demonstrate and compare the performance of multiple RL methods under different risk specifications (risk-averse, risk-neutral, and risk-seeking) through the development and application of an open-source simulation environment. Motivating numerical simulation examples include representative single-zone and multizone building control use cases. Finally, six key insights for future research and broader adoption of RL methods are identified, with specific emphasis on problem features, algorithmic explainability, and solution stability.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Tracing U.S. fuel life-cycle greenhouse gas emissions in a multi-sector dynamics model using LC-GCAM

Model-based analysis of fuel pathways is essential for informing energy and environmental policy. Two major model types are typically used: multi-sector dynamics models, which capture the broader energy-economy, such as GCAM (Global Change Analysis Model), and life cycle assessment models, such as GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation). Each has distinct strengths and limitations, and recent studies increasingly adopt hybrid approaches to harness the advantages of both. However, such integration is often time-consuming and complicated by inconsistencies in system boundaries and technology definitions. We present LC-GCAM, a new tool that enables estimation of life-cycle greenhouse gas emissions and primary energy use for any fuel pathway represented in GCAM. We apply LC-GCAM to 300 scenarios designed to explore key uncertainties affecting the life-cycle performance of future fuel options in the U.S. freight sector. To evaluate LC-GCAM, we compare its results with those from GREET for nine fuel types in a 2030 reference scenario. When input assumptions are modestly aligned, LC-GCAM and GREET estimates typically agree within 10% (absolute sum-based mean absolute percentage error). LC-GCAM offers a flexible and efficient approach to generating life-cycle metrics within an integrated modeling framework, supporting robust policy analysis across a wide range of interacting energy system uncertainties.

Wolfram, Paul↗