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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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Cyclic dynamics of misfires and partial burns in a dilute spark-ignition engine
Here, this study investigates the cyclic dynamics of cumulative heat release data past the edge of stability in a dilute spark-ignition engine. Emphasis is placed on analyzing the cyclic dynamics near the dilute limit where partial burns and misfires are frequent. These events are often followed by a higher-energy cycle due to the feed-forward mechanism present in the residual gases. These patterns are deterministic and increase the coefficient of variation to undesirable levels. Symbol sequence analysis was used to investigate the cyclic dynamics of these low–high patterns. The heat release was partitioned on an energy basis to give physical meaning to each partition and each sequence created when analyzing the symbol sequence results. This partitioning method provided insight into the differences in the dynamics when operating in the misfire or partial burn regime. These differences could impact the control method used.
Polytype selection in the antisolvent-free crystallization of formamidinium lead iodide using alkylammonium chlorides
This work presents a systematic study of how the size of the C 0 –C 4 alkylammonium chloride additive directs the crystallization pathway in antisolvent-free FAPbI 3 films. We find that larger cations promote the intermediate formation of hexagonal polytypes, correlating with enhanced film crystallinity. Understanding the nucleation, crystallization, and phase transition mechanisms in perovskite thin films produced without antisolvent quenching is crucial for improving phase stability and optoelectronic performance at scale. By means of in situ grazing incidence wide-angle X-ray scattering experiments, we show that the nature of alkylammonium chloride (RACl) additives significantly influences the crystallization dynamics and temperature-dependent stability of formamidinium lead iodide (FAPbI 3 ) perovskite films formed using antisolvent-free processes. The in situ experiments reveal that the effective radius of the RA + cation and the vapor pressure of the RA 0 conjugate base are critical factors affecting the crystallization pathway and film morphology. C 2 –C 3 additives with moderate chain lengths may strike a balance between promoting the early crystallization of the cubic 3C phase while suppressing unwanted phases. This balance promotes the formation of highly crystalline cubic perovskite phase with well-oriented structure and better optoelectronic properties. These findings provide guidance regarding the design of additives to crystallize more stable and efficient perovskite films for optoelectronic applications.
Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems
Not Available
On the Dynamic Generation of Megagauss-Level Magnetic Fields to Magnetize and Stabilize Pulsed-Power-Driven Implosions.
Abstract not provided.
Intelligently Partitioned Phasor-EMT Hybrid Simulations of Large-Scale, High-IBR Power Systems
As the penetration level of power electronics-interfaced renewables such as photovoltaics (PV) and wind has surged in modern electric grids, new operational risks caused by the dynamics of those inverter-based resources (IBRs) are emerging in parallel. Lessons learned from various grid events include that the impact of IBRs on system-level grid stability will become prominent along with the increase of renewables and that the short-timescale dynamic impacts of IBRs on grid stability are not fully captured by current commercial dynamic simulation tools [1] [2]. For example, IBRs can be controlled to mitigate those destabilizing interactions, but conventional phasor-domain tools (e.g. PSS/E, PSLF) often cannot capture that; likewise, the existing electromagnetic transient (EMT) simulation tools (e.g. PSCAD, EMTP) can simulate detailed IBR controls, but for large power systems with many IBRs, slow simulation speeds severely impede the ability to study dynamic events [3] [4]. Massively paralleling simulations using high-performance computing (HPC) can help address this, especially now that cloud-based HPC capability is widely available, but today s EMT tools are not HPC-compatible, and parallelization of dynamic simulation solvers is not trivial because each region can dynamically affect the others. Thus, dynamic simulation of grids with very large numbers of IBRs potentially poses a barrier to the ongoing energy transition.
Symbiotic bacteriophages exhibit multiple adaptive strategies in activated sludge flocs and contribute to floc stability
Despite the importance of phages for the dynamics and functions of microbial communities, it remains largely unexplored how symbiotic phages adapt in activated sludge systems and influence microbial aggregate stability, which is critical for clarification and reliable performance. Here, based on 12,127 phage contigs recovered from 12 activated sludge microbiomes, the symbiotic phages exhibited broad host ranges and infected dominant prokaryotes in activated sludge. Moreover, these phage communities exhibited high lysogenicity (49% to 66% lysogenic phages) and harbored diverse auxiliary metabolic genes (AMGs) that could enhance microbial aggregate stability, such as genes coding for lipopolysaccharide, peptidoglycan, and extracellular polysaccharide biosynthesis. Additionally, prokaryotic antiviral systems were widely distributed in dominant prokaryotes, particularly the Restriction-Modification (RM) and CRISPR-Cas systems, which could mitigate potential harmful phage infections. Overall, this study reveals the contemporary beneficial relationship between phages and prokaryotic hosts in activated sludge microbiomes and the potential benefits of symbiotic phages on floc stability.
Sparse Control Synthesis for Uncertain Responsive Loads With Stochastic Stability Guarantees
In this report, recent studies have demonstrated the potential of flexible loads in providing frequency response services, predominantly due to their availability and cost-effectiveness. However, uncertainty and variability in various weather-related and end-use behavioral factors often impact the reliability of demand-side control performance. This work addresses this problem with the design of a demand-side control to achieve frequency response under load uncertainties. Our approach involves modeling the load uncertainties via stochastic processes that appear as both multiplicative and additive in the power system dynamics. Recently developed mean square exponential stability (MSES) results for continuous-time linear stochastic systems are applied to pose the control synthesis problem which results in an LMI-based optimization problem. Additional costs and constraints are added to the LMI-based controller synthesis to ensure MSES, improve closed-loop transient performance, maximize tolerable uncertainties, and promote sparsity in the controller. Additionally, the fundamental limitations between the tolerable uncertainties and control efforts while ensuring MSES are discussed. Further, the control synthesis problem for the case of the full-state measurement is generalized to the case of partial-state measurements. The proposed control synthesis is illustrated on an IEEE 39 bus system with rigorous studies to demonstrate the role of sparsity, closed-loop transient performance, tolerable uncertainties, and control efforts while ensuring MSES and achieving frequency response.
Integrated machine learning-molecular dynamics framework for electrolyte property prediction
Electrochemical stability windows determine the operating range of battery electrolytes, yet accurate prediction remains challenging because stability emerges from statistical ensembles of local solvation environments rather than single ground-state molecular structures. Traditional density functional theory calculations on energy-minimized clusters cannot capture the thermal variations in local coordination environments and geometries that govern decomposition, while SMILES-based machine learning methods lack explicit representation of three-dimensional solvation structure and ion pairing. Here, we introduce a structure-aware machine learning framework that predicts frontier orbital energies (HOMO and LUMO) directly from molecular dynamics-sampled solvation configurations, achieving sub-0.6 eV accuracy at computational costs 3–4 orders of magnitude lower than first-principles methods. Across twelve representative battery electrolytes, we demonstrate that solvent-separated and contact ion pairs exhibit strong size- and local chemistry dependent electronic stability, with variations in coordination shifts of HOMO or LUMO level by 2–3 eV, and that extended solvation structure and partially desolvated environment further modulate stability by up to 3 eV. By encoding the statistical nature of electrochemical failure through ensemble sampling of explicit solvation geometries, our approach enables high-throughput screening and rational design of next-generation battery electrolytes with mechanistic understanding of structure–property relationships.
A Convex Data-Driven Approach for Nonlinear Control Synthesis
We consider a class of nonlinear control synthesis problems where the underlying mathematical models are not explicitly known. We propose a data-driven approach to stabilize the systems when only sample trajectories of the dynamics are accessible. Our method is built on the density-function-based stability certificate that is the dual to the Lyapunov function for dynamic systems. Unlike Lyapunov-based methods, density functions lead to a convex formulation for a joint search of the control strategy and the stability certificate. This type of convex problem can be solved efficiently using the machinery of the sum of squares (SOS). For the data-driven part, we exploit the fact that the duality results in the stability theory can be understood through the lens of Perron–Frobenius and Koopman operators. This allows us to use data-driven methods to approximate these operators and combine them with the SOS techniques to establish a convex formulation of control synthesis. The efficacy of the proposed approach is demonstrated through several examples.
Large-Signal Stability Improvement of Parallel Grid-Forming Inverter-Driven Black Start: Preprint
With the rapid increase of inverter-based resources in modern grids, advanced grid-forming (GFM) inverter capa- bilities, such as system restoration and operation under faults, are urgently needed to realize power electronics-dominant grids at scale. One such capability is inverter-driven black start using GFM inverters. This paper analyzes the ability of two recently proposed advanced GFM controls to help GFM inverters sustain system-wide, off-nominal conditions and remain synchronized until they can overcome the momentary overloading as more GFMs join the process without generator sequence coordination or communications and finally stabilize the grid. Through an extensive set of 1,200 full-order electromagnetic transient simu- lations, we evaluate the black-start process while employing the various GFM inverter controls. The results show that the GFM current limiter and primary control have a significant impact on the stability of the system during dynamic operating conditions and thus impact the success of inverter-driven system restoration.
Measurement and feedback-driven adaptive dynamics in the classical and quantum kicked top
In classical dynamical systems, stochastic feedback can stabilize otherwise unstable periodic orbits, giving rise to distinct controlled and uncontrolled phases as the rate of control application is varied. In this work, we apply these control protocols in classical, semiclassical, and quantum regimes to the kicked top, a paradigmatic model of quantum chaos. The quantum kicked top, modeled as the dynamics of a spin-S object, naturally interpolates between these regimes with the spin size S acting as an effective Planck constant. We show that the dynamics of the kicked top in classical, semiclassical, and fully quantum limits can all be controlled using stochastic feedback protocols. Comparing the full quantum dynamics to a truncated Wigner approximation that captures quantum noise but neglects interference beyond the Ehrenfest time, we find that low-moment observables are largely accounted for semiclassically, while the remaining discrepancy in higher moments is consistent with contributions from interference and possibly nonlinearities in rare trajectories that explore the compact phase space. We also find rapid purification in the numerics studied for all rates of control considered, suggesting that control quenches the top's ability to encode a qubit of quantum information even in the uncontrolled phase.
Thermal energy evolution and mechanical deformation of monocrystalline yttria-stabilized zirconia nanoparticles in aerosol deposition processes
Aerosol deposition (AD) is a coating process wherein aerosol particles are impacted on a target substrate. There are fundamental differences between the AD process (cold impact), where particle translational kinetic energy is high and thermal energy is low, and thermal spray deposition (thermal impact), where translational energy is lower but thermal energy is high. To better compare cold and thermal impact effects on particles, we carried out molecular dynamics simulations using yttria-stabilized zirconia (YSZ) nanoparticles on YSZ substrates as a model system. We performed cold impact simulations at 300 K with variable impact velocity in the 500 ms –1 –1500 ms –1 range to understand how increasing translational kinetic energy affects thermal energy and mechanical evolution. We then performed thermal impact simulations at variable temperature and impact velocity, but where the total kinetic energy of the nanoparticle was equivalent to that of a 300 K, 1000 impact. In cold impact, the temperature increases in YSZ nanoparticles at a rate of 10 13 – 10 14 K s –1 , and large temperature gradients result. Conversely, in thermal impact, nanoparticle temperatures remain uniform. Furthermore, the temperature gradients during cold impact coincide with plastic deformation in nanoparticles, while with larger thermal energies, plastic deformation is reduced.
Voltage cycling as a dynamic operation mode for high temperature electrolysis solid oxide cells
Solid Oxide Electrolysis Cells (SOECs) have emerged as a promising technology for the efficient production of H2 via high-temperature electrolysis. However, power input from dynamic energy sources remains a significant challenge for their long-term stability. It is important to analyze the tolerance of cells under dynamic operation conditions. This study focuses on evaluating the impact of voltage cycling on the performance and durability of electrode-supported SOECs. We explore the operational limits and degradation mechanisms of SOECs subjected to various voltage conditions and find that the cells have high tolerance for dynamic voltage. Voltage cycling between 1.3 V and 1.5 V for 9000 cycles does not damage the cell. Conversely, cycling to higher voltages (≥1.7 V) results in accelerated degradation. Advanced characterization is used to screen for various degradation modes post operation. Within the oxygen electrode, XRD and STEM EDS find compositional and phase evolution in all voltage cycled samples including increased decomposition of the air electrode resulting in cation migration. Microstructural analysis of the fuel electrode from nano-CT data shows minimal change throughout the sample set and no evidence of Ni migration, indicating the fuel electrode is stable and not impacted by cycling to higher voltages within the timeframe studied.
Manipulating Conjugated Polymer Backbone Dynamics through Controlled Thermal Cleavage of Alkyl Side Chains
The morphological stability of an organic photovoltaic (OPV) device is greatly affected by the dynamics of donors and acceptors occurring near the device's operational temperature. These dynamics can be quantified by the glass transition temperature (T g ) of conjugated polymers (CPs). Because flexible side chains possess much faster dynamics, the cleavage of the alkyl side chains will reduce chain dynamics, leading to a higher T g . In this work, the T g s for CPs are systematically studied with controlled side chain cleavage. Isothermal annealing of polythiophenes featuring thermally cleavable side chains at 140 °C, is found to remove more than 95% of alkyl side chains in 24 h, and raise the backbone T g from 23 to 75 °C. Coarse grain molecular dynamics simulations are used to understand the T g dependence on side chain cleavage. X-ray scattering indicates that the relative degree of crystallization remains constantduring isothermal annealing process. The effective conjugation length is not influenced by thermal cleavage; however, the density of chromophore is doubled after the complete removal of alkyl side chains. Here, the combined effect of enhancing T g and conserving crystalline structures during the thermal cleavage process can provide a pathway to improving the stability of optoelectronic properties in future OPV devices.
Effect of Surface Roughness on Dynamic Stall in Pitching Motion
Dynamic stall plays a critical role in determining the performance and stability of a wide range of fluid-dynamics systems in various engineering applications. This unsteady aerodynamic phenomenon is particularly significant for maneuvering aircraft wings, jet aircraft subjected to gust encounters, helicopter rotor blades, and wind turbine blades [1–3]. The prediction of the dynamic stall vortex (DSV) is challenging due to factors such as unsteady aerodynamics, three-dimensional (3-D) effects, turbulence and flow separation, incoming gust, and surface impact effects [4–6]. Hence, advanced computational techniques and modeling approaches in computational fluid dynamics (CFD) would be required to enhance the accuracy and reliability of DSV predictions in dynamic stall scenarios. In the past, Batther and Lee [7] employed delayed detached eddy simulations (DDES) to understand the flow physics associated with the onset of dynamic stall. Their approach demonstrated that DDES achieves results comparable to those obtained from large-eddy simulations at a reduced computational cost. In another study, Khalifa et al. [8] examined the 3-D aspects of dynamic stall on a NACA 0012 airfoil using DES solvers. In conclusion, the findings underlined the superiority of 3-D simulations over two-dimensional approaches, particularly in predicting the lift coefficient values and capturing dynamic stall stages more precisely.
Computationally aided, entropy-driven synthesis of highly efficient and durable multi-elemental alloy catalysts
Multi-elemental alloy nanoparticles (MEA-NPs) hold great promise for catalyst discovery in a virtually unlimited compositional space. However, rational and controllable synthesize of these intrinsically complex structures remains a challenge. Here, we report the computationally aided, entropy-driven design and synthesis of highly efficient and durable catalyst MEA-NPs. The computational strategy includes prescreening of millions of compositions, prediction of alloy formation by density functional theory calculations, and examination of structural stability by a hybrid Monte Carlo and molecular dynamics method. Selected compositions can be efficiently and rapidly synthesized at high temperature (e.g., 1500 K, 0.5 s) with excellent thermal stability. We applied these MEA-NPs for catalytic NH 3 decomposition and observed outstanding performance due to the synergistic effect of multi-elemental mixing, their small size, and the alloy phase. We anticipate that the computationally aided rational design and rapid synthesis of MEA-NPs are broadly applicable for various catalytic reactions and will accelerate material discovery.