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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 757 records · Page 42

Effects of Spatial Resolution on Retropropulsion Aerodynamics in an Atmospheric Environment

Development of a powered descent capability for atmospheric environments is heavily reliant on computational simulation. The prohibitive computational cost of such simulations motivates an improvement in the understanding of the minimum computational fidelity re-quired to accurately characterize aerodynamic-propulsive interference for such applications. This work examines the applicability of detached eddy simulation methods for retropropulsion in atmospheric environments through utilization of a GPU-accelerated computational framework, yielding data that are largely unachievable with conventional high-performance computing resources. This effort was specifically designed to quantitatively assess the effects of spatial resolution on vehicle aerodynamics for nominal operation of a low lift-to-drag ratio, human-scale Mars lander concept. The test matrix and scaling approach span relevant nozzle expansion conditions as well as mid-supersonic to high-subsonic operating conditions. Solutions were generated using computational grids ranging from 143 million to 1.14 billion grid points (degrees of freedom). This paper will provide an overview of the computational campaign, approach, and discussion of preliminary results focused on a range of operating conditions for a conceptual low lift-to-drag, human-scale Mars lander.

Ashley M Korzun↗

Molten sodium batteries: advances in chemistries, electrolytes, and interfaces

The need for clean, renewable energy has driven the expansion of renewable energy generators, such as wind and solar. However, to achieve a robust and responsive electrical grid based on such inherently intermittent renewable energy sources, grid-scale energy storage is essential. The unmet need for this critical component has motivated extensive grid-scale battery research, especially exploring chemistries “beyond Li-ion”. Among others, molten sodium (Na) batteries, which date back to the 1960s with Na-S, have seen a strong revival, owing mostly to raw material abundance and the excellent electrochemical properties of Na metal. Recently, many groups have demonstrated important advances in battery chemistries, electrolytes, and interfaces to lower material and operating costs, enhance cyclability, and understand key mechanisms that drive failure in molten Na batteries. For widespread implementation of molten Na batteries, though, further optimization, cost reduction, and mechanistic insight is necessary. In this light, this work provides a brief history of mature molten Na technologies, a comprehensive review of recent progress, and explores possibilities for future advancements.

25 ENERGY STORAGE↗

Modal control of an unstable periodic orbit

Floquet theory is applied to the problem of designing a control system for a satellite in an unstable periodic orbit. Expansion about a periodic orbit produces a time-periodic linear system, which is augmented by a time-periodic control term. It is shown that this can be done such that (1) the application of control produces only inertial accelerations, (2) positive real Poincareexponents are shifted into the left half-plane, and (3) the shift of the exponent is linear with control gain. These developments are applied to an unstable orbit near the earth-moon L(3) point pertubed by the sun. Finally, it is shown that the control theory can be extended to include first order perturbations about the periodic orbit without increase in control cost.

Wiesel, W.↗

Effects of Climate Change on Capacity Expansion Decisions of an Electricity Generation Fleet in the Southeast U.S.

The electric power sector in the United States faces many challenges related to climate change. On the demand side, climate change could shift demand patterns due to increased air temperatures. On the supply side, climate change could lead to deratings of thermal units due to changes in air temperature, water temperature, and water availability. Past studies have typically analyzed these risks separately. Here, we developed an integrated, multimodel framework to analyze how compounding risks of climate-change impacts on demand and supply affect long-term planning decisions in the power system. In the southeast U.S., we found that compounding climate-change impacts could result in a 35% increase in installed capacity by 2050 relative to the reference case. Participation of renewables, particularly solar, in the fleet increased, driven mostly by the expected increase in summertime peak demand. Such capacity requirements would increase investment costs by approximately 31 billion (USD 2015) over the next 30 years, compared to the reference case. These changes in investment decisions align with carbon emission mitigation strategies, highlighting how adaptation and mitigation strategies can converge.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Conservative projection-based data-driven model order reduction of a fluid-kinetic spectral solver

Kinetic simulations are computationally intensive due to six-dimensional phase space discretization. Many kinetic spectral solvers use the asymmetrically weighted Hermite expansion due to its conservation and fluid-kinetic coupling properties, i.e., the lower-order Hermite moments capture and describe the macroscopic fluid dynamics, and higher-order Hermite moments describe the microscopic kinetic dynamics. We leverage this structure by developing a parametric data-driven reduced-order model based on the proper orthogonal decomposition, which projects the higher-order kinetic moments while retaining the fluid moments intact. We demonstrate analytically and numerically that the method ensures local and global mass, momentum, and energy conservation. The numerical results show that the proposed method effectively replicates the high-dimensional spectral simulations at a fraction of the computational cost and memory, as validated on the weak Landau damping and two-stream instability benchmark problems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Observer-Based Magnetic Bearing Controller Developed for Aerospace Flywheels

A prototype of a versatile, observer-based magnetic bearing controller for aerospace flywheels was successfully developed and demonstrated on a magnetic bearing test rig (see the photograph) and an actual flywheel module. The objective of this development included a fast, yet low risk, control development process, and a robust, high-performance controller for a large variety of flywheels. This required a good system model, an efficient development procedure, and a model-based controller that addressed the key problems associated with flywheel and bearing imbalance, sensor error, and vibration. The model used in this control development and tuning procedure included the flexible rotor dynamics and motor-induced vibrations. Such a model was essential for low-risk scheduling of speed-dependent control parameters and for reliable evaluation of novel control strategies. The successfully tested control prototype utilized an extended Kalman filter to estimate the true rotor principal-axis motion from the raw sensor position feedback. For control refinement, the extended Kalman filter also estimated and eliminated the combined effects of mass-imbalance and sensor runouts from the input data. A key advantage of the design based on the extended Kalman filter is its ability to accurately estimate both the rotor's principal-axis position and gyroscopic rates with the least amount of phase lag. This is important for control parameter scheduling to dampen the gyroscopic motions. Because of large uncertainties in the magnetic bearing and imbalance characteristics, this state-estimation scheme alone is insufficient for containing the rotor motion within the desired 1-mil excursion radius. A nonlinear gain adjustment based on an estimation of the principal-axis orbit size was needed to provide a coarse (nonoptimal), but robust, control of the orbit growth. Control current minimization was achieved with a (steepest gradient) search of synchronous errors in the principal-axis position input data. Actual flywheel tests of this observer-based controller (developed entirely in-house) at the NASA Glenn Research Center showed that the model correctly predicted the rotor orbit growth as a function of rotational speed, and it demonstrated the capability of gain adjustments to arrest this growth. Data from these tests on an actual flywheel module spun to 26,000 rpm proved that the controller was able to contain the shaft motion to within much less than 0.5 mils of radial excursion with axis currents less than 300 mA in root-mean-square estimate. The test speed range was limited because of thermal expansion concerns for this particular flywheel unit, not because of any deficiency in the controller. Simulations for this unit indicated that the controller should be robust up to its top operating speed of 60,000 rpm. Aside from these important achievements, and most significantly, it took less than 1 week to adapt this controller from the simple test rig to the actual flywheel and to demonstrate full five-axis levitation and control. This demonstration showed that both the controller and the model-based development and tuning framework are easily adaptable to a wide range of rotors and bearing configurations and, hence, are capable of reducing design risks and costs for many future flywheel technology developments.

Le, Dzu K.↗

A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems

Constructing surrogate models for uncertainty quantification (UQ) on complex partial differential equations (PDEs) having inherently high-dimensional O(10 n ), n ≥ 2, stochastic inputs (e.g., forcing terms, boundary conditions, initial conditions) poses tremendous challenges. The “curse of dimensionality” can be addressed with suitable unsupervised learning techniques used as a pre-processing tool to encode inputs onto lower-dimensional subspaces while retaining its structural information and meaningful properties. In this work, we review and investigate thirteen dimension reduction methods including linear and nonlinear, spectral, blind source separation, convex and non-convex methods and utilize the resulting embeddings to construct a mapping to quantities of interest via polynomial chaos expansions (PCE). Here, we refer to the general proposed approach as manifold PCE (m-PCE), where manifold corresponds to the latent space resulting from any of the studied dimension reduction methods. To investigate the capabilities and limitations of these methods we conduct numerical tests for three physics-based systems (treated as black-boxes) having high-dimensional stochastic inputs of varying complexity modeled as both Gaussian and non-Gaussian random fields to investigate the effect of the intrinsic dimensionality of input data. We demonstrate both the advantages and limitations of the unsupervised learning methods and we conclude that a suitable m-PCE model provides a cost-effective approach compared to alternative algorithms proposed in the literature, including recently proposed expensive deep neural network-based surrogates and can be readily applied for high-dimensional UQ in stochastic PDEs.

42 ENGINEERING↗

Characterization of ablator dynamics initiated by picket-pulse conditions

Direct-drive laser-fusion targets may include low-cost plastic ablator material. Here, this work describes a tabletop experimental system designed to enable the investigation of the interaction of such ablator material as a function of the laser wavelength and the associated excitation mechanism. This experimental system offers time-resolved imaging of the material response with subpicosecond temporal resolution and ≈1-μm spatial resolution. The system involves synchronized femtosecond and picosecond lasers to initiate and characterize the dynamics of polystyrene planar targets irradiated by conditions similar to a “picket” prepulse from a direct-drive pulse shape. Ultrafast imaging of the initial excitation process followed by the resulting critical plasma formation, shock-wave propagation, and blowoff plasma expansion is used to compare the impact of excitation wavelength, 355 nm or 266 nm, on the ensuing dynamics. This work aims to investigate fundamental laser material interactions to help advance physics models that can inform the development of laser driver and target designs.

42 ENGINEERING↗

Assessing the Expansion of Ground-Motion Sensing Capability in Smart Cities via Internet Fiber-Optic Infrastructure

Monitoring ground motion in smart cities can improve the public safety by providing critical insights on natural and anthropogenic hazards, for example, earthquakes, landslides, explosions, infrastructure failures, and so forth. Although seismic activity is typically measured using dedicated point sensors (e.g., geophones and accelerometers), techniques such as distributed acoustic sensing have demonstrated the utility of using fiber-optic cable to detect seismic activity over comparable distances. In this article, we present the results of a study that quantifies the expansion in an area monitored for low-amplitude ground-motion events by augmenting existing point sensors with the internet fiber-optic cable infrastructure. Here we begin by describing our methodology, which utilizes geospatial data on point sensors and internet optical fiber deployed in metropolitan statistical areas (MSAs) in the United States. We extend these data to identify the area that can be monitored by (1) considering the observed seismic noise data in target locations, (2) applying the model from Wilson et al. (2021) to understand the potential coverage area gains using optical fiber sensing, and (3) optimizing the selection of fiber segments to maximize coverage and minimize deployment costs. We implement our methodology in ArcGIS to assess the additional area that can be monitored for low-amplitude ground-motion events (i.e., magnitude >0.5) by utilizing internet fiber-optic cables in the 100 most populous MSAs in the United States. We find that the addition of internet fiber-based sensors in MSAs would increase the area monitored on average by over an order of magnitude from 1% to 12%, if the subset of fiber cable segments that maximize coverage and minimize deployment costs is chosen even if only 20% of all fibers are used.

58 GEOSCIENCES↗

Renewable Energy and Efficiency Technologies in Scenarios of U.S. Decarbonization in Two Types of Models: Comparison of GCAM Modeling and Sector-Specific Modeling

Energy system projections from quantitative models inform actions ranging from short-term and local decisions, such as those about technology and infrastructure deployment, and global and long-term negotiations and targets. Computational limits require model designers to balance coverage and resolution (i.e., breadth versus depth). Some models, such as the Global Change Analysis Model (GCAM), represent all energy sources and uses with less resolution than models that focus on a single sector's energy use. GCAM balances global supply and demand of all energy carriers projecting prices using internal calculations for energy sources and costs of greenhouse gas mitigation while capturing interlinkages between the energy system, water, agriculture and land use, the economy, and the climate. This globally comprehensive model was used to frame the Long-Term Strategy of the United States: Pathways to Net-Zero Greenhouse Gas Emissions by 2050, which the White House released in 2021 and has been used to inform national and global economy-wide climate change mitigation discussions and strategy development for decades. Unlike GCAM, sectoral models focus on a portion of the energy sector and with greater detail and resolution. The Regional Energy Deployment System (ReEDS) electricity-sector model, for example, projects electricity system capacity expansion and operation with high-fidelity representation of emerging technologies for deep decarbonization, such as variable renewable energy and energy storage, and integration of these technologies into the electric grid. The Transportation Energy and Mobility Pathway Options (TEMPO) transportation-sector model enables analysis of household choices, with a focus on adoption, charging, and use of electric vehicles. The Scout buildings-sector model supports detailed consideration of the policies and markets that can accelerate the adoption of electrification and energy conservation measures in buildings. Such sector-specific models are instrumental in informing technology research, sectoral planning strategies, and sector-specific aspects of greenhouse gas mitigation strategies in the United States. The integrated multisector and sector-specific modeling approaches represented by GCAM and these sectoral models are complementary. The integrated multisector approach calculates energy pricing and resource allocation within the model, which is important for consistency when future conditions substantially diverge from current conditions in transformative scenarios. The sector-specific approach facilitates representation of granular details across spatial, temporal, technological, and market dimensions that enable exploration of particular interactions and trade-offs. This report presents the results of recent work to explore the differences and trade-offs between these approaches by comparing GCAM with the sector-specific ReEDS, TEMPO, and Scout models. The report compares both model structures and results, and it addresses their potential relevance and applications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Exploring Class 8 Long-Haul Truck Electrification: Key Technology Evaluation and Potential Challenges

The phenomena of global warming and climate change are encouraging more and more countries, local communities, and companies to establish carbon neutrality targets, which has very significant implications for the US trucking industry. Truck electrification helps fleets to achieve zero tailpipe emissions and macro-scale decarbonization while allowing continued business growth in response to the rapid expansion of e-commerce and shipping related to increased globalization. Here, this paper presents an analysis of Class 8 long-haul truck electrification using a commercial vehicle electrification evaluation tool and Fleet DNA drive data. The study provides new insight into the impacts of streamlined chassis, battery energy density, and superfast charging on battery capacity needs as well as implications for payload, energy consumption, and greenhouse gas emissions for electric long-haul trucks. The study also identifies a pathway for achieving optimal long-haul truck electrification. The results show that no single technology can simultaneously achieve significant improvements in all these key areas and that a cost-effective approach requires a deliberate combination of all of these technologies. Moreover, the analysis highlights the strong evolution of vehicle and battery technologies and the excellent potential for long-haul truck electrification to achieve 100% year-round service coverage with continued technology advancement.

33 ADVANCED PROPULSION SYSTEMS↗

Solution of the minimum time-to-climb problem by matched asymptotic expansions

Application of singular perturbation techniques to trajectory optimization problems of flight mechanics is discussed. The method of matched asymptotic expansions is used to obtain an approximate solution to the aircraft minimum time-to-climb problem. Outer, boundary-layer, and composite solutions are obtained to zeroth and first orders. A stability criterion is derived for the zeroth-order boundary-layer solutions (the theory requires a form of boundary-layer stability). A numerical example is considered for which it is shown that the stability criterion is satisfied and a useful numerical solution is obtained. The zeroth-order solution proves to be a poor approximation, but the first-order solution gives a good approximation for both the trajectory and the minimum time-to-climb. The computational cost of the singular perturbation solution is considerably less than that of a steepest descent solution. Thus singular perturbation methods appear to be promising for the solution of optimal control problems.

Ardema, M. D.↗

Design, Fabrication, and Calibration of an Embedded Piezoceramic Actuator for Active Control Applications

In this presentation, the authors describe the design and fabrication processes for a PZT strain actuator that evolved during the initial stages of a research effort to synthesize and process intelligent, cost effective structures (SPICES). The actuator performance requirements were similar to those of conventional actuators, e.g., it had to be robust, highly efficient with adequate force and stroke, as lightweight as possible, and most importantly, affordable. Further, since the actuator was to be integrated within a composite structure, it had to be compatible with the host material and easily embeddable during the fabrication process. In control applications employing strain devices as actuators, a good bond between this actuator and host material is critical to their successful operation. This criterion is often difficult to achieve when attempting to join ceramics with metals or polymers with dissimilar properties such as Young's moduli, thermal expansion coefficients, etc. One unique feature of the actuator design that evolved in this project is that the need for direct bonding between the PZT ceramic and polymers was circumvented, i.e. the strain transfer to the host material was achieved via a frame surrounding the ceramic. Consequently, the frame material could be selected (or coated) for compatibility with the host material. A second feature is that the frame enclosed a co-fired, multilayered, PZT stack that was used to minimize the voltage requirements while maximizing the output strain.

Koopmann, Gary H.↗

Advanced Aero-Propulsive Mid-Lift-to-Drag Ratio Entry Vehicle for Future Exploration Missions

NASA is currently looking well into the future toward realizing Exploration mission possibilities to destinations including the Earth-Moon Lagrange points, Near-Earth Asteroids (NEAs) and the Moon. These are stepping stones to our ultimate destination Mars. New ideas will be required to conquer the significant challenges that await us, some just conceptions and others beginning to be realized. Bringing these ideas to fruition and enabling further expansion into space will require varying degrees of change, from engineering and integration approaches used in spacecraft design and operations, to high-level architectural capabilities bounded only by the limits of our ideas. The most profound change will be realized by paradigm change, thus enabling our ultimate goals to be achieved. Inherent to achieving these goals, higher entry, descent, and landing (EDL) performance has been identified as a high priority. Increased EDL performance will be enabled by highly-capable thermal protection systems (TPS), the ability to deliver larger and heavier payloads, increased surface access, and tighter landing footprints to accommodate multiple asset, single-site staging. In addition, realizing reduced cost access to space will demand more efficient approaches and reusable launch vehicle systems. Current operational spacecraft and launch vehicles do not incorporate the technologies required for these far-reaching missions and goals, nor what is needed to achieve the desired launch vehicle cost savings. To facilitate these missions and provide for safe and more reliable capabilities, NASA and its partners will need to make ideas reality by gaining knowledge through the design, development, manufacturing, implementation and flight testing of robotic and human spacecraft. To accomplish these goals, an approach is recommended for integrated development and implementation of three paradigm-shifting capabilities into an advanced entry vehicle system with additional application to launch vehicle stage return, thus making ideas reality. These paradigm shifts include the technology maturation of advanced flexible thermal protection materials onto mid lift-to-drag ratio entry vehicles, the development of integrated supersonic aero-propulsive maneuvering, and the implementation of advanced asymmetric launch shrouds. These paradigms have significant overlap with launch vehicle stage return already being developed by the Air Force and several commercial space efforts. Completing the realization of these combined paradigms holds the key to a high-performing entry vehicle system capability that fully leverages multiple technology benefits to accomplish NASA's Exploration missions to atmospheric planetary destinations.

Campbell, C. H.↗

Gradient-informed Hamiltonian Monte Carlo for multicomponent CALPHAD model optimization and uncertainty quantification

CALPHAD model parameter optimization is inherently challenging due to non-smooth objective functions, high-dimensional parameter spaces, and the need for uncertainty quantification (UQ). Traditional weighted nonlinear least squares approaches are computationally efficient but local, whereas black-box global optimizers and ensemble Markov Chain Monte Carlo (MCMC) methods provide broader exploration at substantial computational cost. The objective of this work is to combine the global exploration capability of gradient-informed Hamiltonian Monte Carlo – specifically the No-U-Turn Sampler (NUTS) – with local deterministic refinement using BFGS to efficiently optimize multicomponent CALPHAD models with minimal manual intervention. Analytic gradients are computed via the Jansson derivative framework. The methodology is demonstrated on the Cr—Fe binary system and extended to the Cr—Fe—Ni ternary system with 32 degrees of freedom. For Cr—Fe, NUTS achieves comparable or superior optimality relative to ensemble MCMC while requiring over an order-of-magnitude fewer likelihood evaluations. Parameter uncertainties are quantified through NUTS sampling and propagated to thermodynamic observables using local expansion, demonstrating a novel modular approach that combines binary and ternary parameter subsets without requiring global relaxation. These results establish gradient-informed exploration as a scalable strategy for multicomponent CALPHAD optimization and provide a practical route towards efficient higher-order database development with quantified uncertainty.

36 MATERIALS SCIENCE↗

STE thrust chamber technology: Main injector technology program and nozzle Advanced Development Program (ADP)

The purpose of the STME Main Injector Program was to enhance the technology base for the large-scale main injector-combustor system of oxygen-hydrogen booster engines in the areas of combustion efficiency, chamber heating rates, and combustion stability. The initial task of the Main Injector Program, focused on analysis and theoretical predictions using existing models, was complemented by the design, fabrication, and test at MSFC of a subscale calorimetric, 40,000-pound thrust class, axisymmetric thrust chamber operating at approximately 2,250 psi and a 7:1 expansion ratio. Test results were used to further define combustion stability bounds, combustion efficiency, and heating rates using a large injector scale similar to the Pratt & Whitney (P&W) STME main injector design configuration including the tangential entry swirl coaxial injection elements. The subscale combustion data was used to verify and refine analytical modeling simulation and extend the database range to guide the design of the large-scale system main injector. The subscale injector design incorporated fuel and oxidizer flow area control features which could be varied; this allowed testing of several design points so that the STME conditions could be bracketed. The subscale injector design also incorporated high-reliability and low-cost fabrication techniques such as a one-piece electrical discharged machined (EDMed) interpropellant plate. Both subscale and large-scale injectors incorporated outer row injector elements with scarfed tip features to allow evaluation of reduced heating rates to the combustion chamber.

Source record↗

Innovative, High-Pressure, Cryogenic Control Valve: Short Face-to-Face, Reduced Cost

A control valve that can throttle high-pressure cryogenic fluid embodies several design features that distinguish it over conventional valves designed for similar applications. Field and design engineers worked together to create a valve that would simplify installation, trim changes, and maintenance, thus reducing overall cost. The seals and plug stem packing were designed to perform optimally in cryogenic temperature ranges. Unlike conventional high-pressure cryogenic valves, the trim size can be changed independent of the body. The design feature that provides flexibility for changing the trim is a split body. The body is divided into an upper and a lower section with the seat ring sandwiched in between. In order to maintain the plug stem packing at an acceptable sealing temperature during cryogenic service, heat-exchanging fins were added to the upper body section. The body is made of stainless steel. The seat ring is made of a nickel-based alloy having a coefficient of thermal expansion less than that of the body material. Consequently, when the interior of the valve is cooled cryogenically, the body surrounding the seat ring contracts more than the seat ring. This feature prevents external leakage at the body-seat joint. The seat ring has been machined to have small, raised-face sealing surfaces on both sides of the seal groove. These sealing surfaces concentrate the body bolt load over a small area, thereby preventing external leakage. The design of the body bolt circle is different from that of conventional highpressure control valves. Half of the bolts clamp the split body together from the top, and half from the bottom side. This bolt-circle design allows a short, clean flow path, which minimizes frictional flow losses. This bolt-circle design also makes it possible to shorten the face-toface length of the valve, which is 25.5 in. (65 cm). In contrast, a conventional, high-pressure control valve face-to-face dimension may be greater than 40 in. (>1 m) long.

Source record↗

Bi-Level Adaptive Storage Expansion Strategy for Microgrids Using Deep Reinforcement Learning

Battery energy storage (BES) is a versatile resource for the secure and economic operation of microgrids (MGs). Prevailing stochastic optimization-based approaches for BES expansion planning for MGs are computationally complicated. This work proposes a data-driven bi-level multi-period BES expansion planning framework to determine the siting, sizing, and timing of BES installations. The proposed planning framework unifies deep reinforcement learning (DRL) and linear programming, thereby decoupling the determinations for the integer and continuous decision variables in two time scales, respectively. In the upper level, a rainbow DRL agent with quantile regression is trained to provide dynamic planning policies to accommodate stochastic renewable energy resources (RESs), load, and battery price changes efficiently. Further, the lower level computes the optimal operation of MGs with frequency constraints to hedge the islanding contingency. The two levels communicate with one another by exchanging storage configuration and operating expenses in order to accomplish the shared goal of minimizing investment and operation costs. Comparative case studies on an MG are carried out to demonstrate the superiority of the proposed DRL-based solution to the mixed-integer linear programming counterpart on efficiency, scalability, and adaptability.

24 POWER TRANSMISSION AND DISTRIBUTION↗