SEARCH · Search NASA

Results for “process optimization and control”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6

All sources NASA NTRS DOE OSTI

DOE OSTI2025

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

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

Hydrophysics↗

DOE OSTI2025

Active oversight and quality control in standard Bayesian optimization for autonomous experiments

The fusion of experimental automation and machine learning has catalyzed a new era in materials research, prominently featuring Gaussian Process (GP) Bayesian Optimization (BO) driven autonomous experiments. Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on real-time assessments of the raw experimental data. This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training. We also incorporate a flexible, human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results. We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data. This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings, providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Non-Equilibrium Actinide Radiation Chemistry and the Nuclear Fuel Cycle

Invited John and Naomi Fackler Lectureship in Chemistry and English seminar at Valparaiso University, IN, USA. Actinides are inherently unstable elements that frequently coexist with other radioisotopes, generating intense ionizing radiation fields that drive the formation of non-equilibrium oxidation states. These transient species exert a profound mechanistic influence on the radiation response of actinide-containing systems due to their unique redox chemistry. Despite their importance, they remain poorly understood, yet such insight is essential for advancing actinide science and accurately predicting radiation-driven behavior. Actinide separations—critical for nuclear energy technologies, strategic deterrence, space exploration, and nuclear medicine—depend on precise control of actinide oxidation states to recover targeted elements from complex matrices such as used nuclear fuel. However, during these processes, actinides, their coordination complexes, and the separation media are all exposed to intense, multicomponent (alpha, beta, gamma, etc.) radiation fields that can alter process efficiency, selectivity, and chemical stability. Understanding, controlling, and mitigating radiation-induced reactions is therefore key to innovating and optimizing next-generation separation technologies. This seminar will provide an overview of the nuclear fuel cycle and non-equilibrium actinide radiation chemistry in the context of recovering actinides from used nuclear fuel, with a particular emphasis on direct-dissolution–based reprocessing strategies. We will explore time-resolved electron pulse radiolysis and gamma dose accumulation studies to elucidate the molecular-level roles of radiation-driven, non-equilibrium actinide species in process performance and in the radiolytic stability of organic ligands used for actinide recovery. These insights offer new pathways for designing advanced separation methods and next-generation solvent systems, with broad implications for the future of the nuclear fuel cycle.

37 - INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL C↗

DOE OSTI2025

Lewis Acid Site Engineering in Chromite Spinels Orchestrated Surface Reconstruction and Surpasses RuO 2 in Oxygen Evolution

Atomic-scale engineering of chromite spinels featuring redox-active tetrahedral A-sites and strong Cr–O covalency offers a promising route to superior platinum-group-metal-free oxygen evolution reaction (OER) catalysts. However, comprehensive studies addressing how cation substitution influences surface chemistry and governs OER activity and durability in chromite spinels remain limited. Here, in this work, a systematic investigation of the multicationic chromite series Ni x Fe y Cr 3−x−y O 4 is presented, identifying composition-dependent Lewis acidity as a descriptor of superior OER performance. It is further demonstrated that tuning surface acidity directly controls dynamic reconstruction processes and lattice-oxygen participation during spinel-based electrocatalysis. Following activation, the optimized Ni 0.8 Fe 0.3 Cr 1.9 O 4 catalyst delivers a current density of 10 mA cm −2 at an overpotential of 235 mV, surpassing RuO 2 , with excellent long-term stability. Integrating microscopic and spectroscopic analysis with operando impedance spectroscopy, it shows that activation generates an oxyhydroxide overlayer and reveals a previously unrecognized link between surface Lewis acidity and the growth kinetics and activity of these shells. Density functional theory calculations indicate that Fe incorporation at octahedral sites raises the O 2p-band center and lowers oxygen-vacancy formation energy, promoting lattice-oxygen activation and triggering reconstruction, yielding enhanced OER. This work integrates cation-driven surface-acidity modulation, acidity-governed reconstruction, and OER activity enhancement into a unified predictive framework for designing earth-abundant spinel-based catalysts.

operando impedance spectroscopy↗

DOE OSTI2024

Real-Time Gear-Shift Optimization for an Autonomous Wheel Loader

Off-road vehicles, such as wheel loaders, consume a significant amount of fuel during the transportation of materials. The gear-shifting process is crucial in fuel savings for transportation, and therefore, optimization of gearshifts is important for vehicle control. For an autonomous off-road vehicle, there is potential for more fuel savings by proper coordination of gearshift optimization and optimization of other control inputs. This brief proposes a new method for integrating gear-shifting into transportation optimization for an autonomous wheel loader to minimize fuel consumption. Furthermore, tests conducted on short loading cycles show that this method can save around 10%–20% of fuel on average compared with a conventional gearshift-scheduling method.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

A Neural Optimizer With Decision-Focused Learning for Optimal Energy Storage Operation

Here, this article introduces a neural optimizer-based framework for optimizing battery energy storage system (BESS) control for grid services, including demand charge and energy cost reduction. By leveraging decision-focused learning (DFL), the proposed framework ensures seamless integration and adaptation, significantly enhancing control performance. A patch time-series transformer is employed for peak load forecasting, incorporating aleatoric uncertainty quantification to account for forecasting uncertainties within the decision-making process. The framework utilizes a solver-in-the-loop approach to generate optimal BESS actions, which are then used to train the neural optimizer-based agent. By co-optimizing both BESS operational modes and output power within the NN, the system achieves improved performance and robustness. After initial training, the forecasting and control models are jointly fine-tuned to account for forecasting errors, further improving decision precision and efficiency through DFL. Case studies are performed to validate the performance of the framework using multiple real-world datasets, demonstrating superior performance in monthly peak load forecasting compared to state-of-the-art models. In addition, the results are compared against existing decision-making approaches. The results demonstrate a reduction in monthly peak forecasting error by approximately 15% across various performance measures and achieve an optimization gap for BESS operation that is about three times smaller compared to existing methods.

Kim, Hyeonjin [Pacific Northwest National Laborato↗

DOE OSTI2024

Ontologies at Work: Analyzing Information Requirements for Model Predictive Control in Buildings

Model Predictive Control (MPC) has shown significant potential for improving energy efficiency, indoor air quality and occupant comfort of buildings. MPC-based control algorithms have also shown the ability to shift loads and optimize for multiple objectives, including but not limited to reducing the green-house gas emissions, energy costs and peak demand. However, one of the main implementation challenges of these control algorithms is the integration and configuration effort needed to deploy a supervisory MPC controller in a building. By assigning standardized references to information sources and control points in buildings, existing studies have shown that semantic ontologies and corresponding queries have the potential to ease the deployment of such controllers. Yet, the use of semantic information to ease the deployment processes of MPC controllers is still limited. In this paper, we review three MPC experiments and synthesize the information requirements of these optimization problems. We then turn to existing and upcoming semantic ontologies such as Brick, SAREF and ASHRAE Standard 223 to represent these requirements, evaluating their potential to support the implementation of an MPC controller. This investigation concludes with a discussion of existing opportunities and open questions that the community should explore to support more streamlined MPC implementations.

Prakash, Anand Krishnan↗

DOE OSTI2025

Optoelectronic polymer memristors with dynamic control for power-efficient in-sensor edge computing

Abstract As the demand for edge platforms in artificial intelligence increases, including mobile devices and security applications, the surge in data influx into edge devices often triggers interference and suboptimal decision-making. There is a pressing need for solutions emphasizing low power consumption and cost-effectiveness. In-sensor computing systems employing memristors face challenges in optimizing energy efficiency and streamlining manufacturing due to the necessity for multiple physical processing components. Here, we introduce low-power organic optoelectronic memristors with synergistic optical and mV-level electrical tunable operation for a dynamic “control-on-demand” architecture. Integrating signal sensing, featuring, and processing within the same memristors enables the realization of each in-sensor analogue reservoir computing module, and minimizes circuit integration complexity. The system achieves 97.15% fingerprint recognition accuracy while maintaining a minimal reservoir size and ultra-low energy consumption. Furthermore, we leverage wafer-scale solution techniques and flexible substrates for optimal memristor fabrication. By centralizing core functionalities on the same in-sensor platform, we propose a resilient and adaptable framework for energy-efficient and economical edge computing.

Optics↗

DOE OSTI2026

Texture development in magnetostrictive Fe-Ga alloys processed by laser powder bed fusion

Iron-gallium (Fe-Ga, Galfenol) alloys are promising magnetostrictive materials for actuators, sensors, and energy harvesting, but their performance is highly sensitive to microstructure and texture. Additive manufacturing by laser powder bed fusion (LPBF) offers a pathway to engineer texture and integrate functional materials into complex geometries. Here, we fabricate Fe-Ga alloys (Fe 82.2 Ga 17.8 ) by LPBF of gas-atomized powders and systematically optimize laser power and scan speed to maximize density and control texture. Nearly full-density parts (up to 99.6 %) are achieved within a narrow processing window. Electron backscatter diffraction (EBSD) reveals a strong <100> fiber texture aligned with the build direction and columnar grains up to 1 mm long. Magnetostriction measurements show saturation magnetostriction of 190 ppm in the build direction. Correlating texture data with macroscopic magnetostriction, we estimate intrinsic magnetostriction constants (λ 100 = 228 ppm, λ 111 = 12 ppm), closely matching single crystal-derived values. These results demonstrate the critical interplay between processing, texture, and functional performance in additively manufactured Fe-Ga alloys and establish LPBF as a viable route for high-performance magnetostrictive materials.

Additive manufacturing↗

DOE OSTI2025

Screw-Dislocation-Driven Growth of 2D Perovskite Spiral Microplates

Two-dimensional (2D) organic–inorganic halide perovskites are solution-processable semiconductors that are promising for optoelectronic applications. Understanding crystallization mechanisms to achieve control over nanostructures is important for optimizing desired properties. Here we introduce a versatile strategy to synthesize spiral microplates of diverse 2D perovskites at the air–water interface through screw-dislocation-driven growth. Spirals of 11 2D perovskite compositions (LA) 2 (A) n−1 Pb n X 3n+1 with different spacer (LA) cations, A-cations, halide (X) anions, and n-number can be grown. They typically consist of single- or few-layer perovskite step heights but exhibit stacking complexity when multiple dislocations interact. The spiral microplates exhibit the characteristic optical properties (photoluminescence and second-harmonic generation) of the underlying 2D perovskites. Fluorescence-detected circular dichroism imaging shows that the chirality of the spiral center does not translate to the observed chiroptical properties of the microplate, consistent with the length scale of the chiral distortion. Furthermore, this solution growth of perovskite spirals diversifies the perovskite microstructures for optoelectronics and other applications.

2D halide perovskites↗

DOE OSTI2025

Safe Reinforcement Learning-Based Transient Stability Control for Islanded Microgrids With Topology Reconfiguration

This paper proposes a safe reinforcement learning (RL)-based transient stability emergency control (TSEC) method for islanded microgrids. RL requires extensive interaction with the environment to learn control strategies, hence, a data-driven approach is used as a substitute for time-consuming time-domain simulation calculations. Deep sigma point processes (DSPP), which is a Gaussian process model, is utilized to predict the normal distribution of transient stability of microgrids and to construct a transient stability chance constraint. Reward-constrained policy optimization (RCPO) can simultaneously achieve objective prediction, policy learning, and constraint cost coefficient update across multiple timescales. RCPO interacts with the DSPP-based microgrid environment through a multi-process parallel manner, greatly increasing the training speed. Case studies on a real islanded microgrid demonstrate that the proposed method can efficiently and quickly obtain the optimal emergency control strategy while adhering to all hard constraints.

14 SOLAR ENERGY↗

DOE OSTI2026

The emergent photophysics and photochemistry of molecular polaritons: a theoretical and computational investigation (Final Technical Report)

When molecules are placed between closely spaced mirrors, they interact strongly with the photons that are trapped between them, generating new quantum states which are no longer exclusively material nor photonic alone, but rather, coherent superpositions of both. These hybrid states are known as molecular polaritons, given that they arise from the strong interaction between the electric field of light and the electrical polarization of the molecules. Recently, experimental advances in nano‐ and microfabrication of molecular polariton architectures have successfully demonstrated their feasibility to control the rate and outcome of a certain class of chemical reactions in condensed phases. Importantly, these reactions proceed in strongly dissipative environments such as liquid solvents and lossy mirrors that allow for photons to escape from their confinement. The purpose of this research is to formulate quantum mechanical theories and computational tools that can elucidate the origin of these intriguing phenomena and simultaneously predict capabilities that this new generation of molecular materials affords. Attention is placed on harnessing polaritons to carry out photophysics and photochemistry that challenge currently existing paradigms, such as the optimization of energy conversion processes in organic solar cell or light‐emitting devices, or unconventional phenomena such as long‐range excitation energy transfer, remote control of chemical reactions, and a new quantum mechanical regime of chemical reactivity due to wavefunction overlaps amongst a large number of molecular polaritons (Bose condensation). This research explores a frontier of Chemistry and Physics where electrons, vibrations, and photons interact strongly with each other to generate emergent behavior that can be creatively exploited to address contemporary challenges in Basic Energy Sciences.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Multilevel Analysis of Electrochemically Mediated Methanolysis of Poly(ethylene terephthalate) (PET)

Here, this study presents a multilevel analysis of electrochemically mediated methanolysis as a promising method for reducing the environmental impacts of plastic recycling, with a focus on depolymerizing poly(ethylene terephthalate) (PET) into dimethyl terephthalate (DMT). Instead of conventional chemical PET depolymerization, this electrochemical approach provides distinct technical advantages in process control and efficiency. At the process level, key operational parameters, including applied current and reaction time, were systematically investigated to optimize PET conversion and DMT selectivity. The electrochemical approach was directly compared to equivalent chemical methanolysis systems and demonstrated superior performance in terms of PET conversion and DMT selectivity. Building on these findings, a technoeconomic assessment identified the current economic bottlenecks and revealed that improvements in process design, DMT selectivity, PET conversion, and energy efficiency are key to reducing the overall process cost and enabling future implementation. While further optimization is required for market competitiveness, these results establish a performance baseline for the electrochemically mediated PET methanolysis process and underscore the importance of combining process-level innovation with systems-level evaluation in the development of sustainable recycling technologies.

chemical recycling↗

DOE OSTI2024

Characterization of Tunnel Oxides in TOPCon Solar Cells

The 1.12 nm thickness for the tunnel oxide layer is near the optimal range described by Choi et al. This thickness should be effective at enabling quantum tunneling; however, it is slightly lower than the reported optimal range which could negatively impact the passivation of the poly-Si interface. An appropriate balance between the two functions must be met to optimize efficiency. Follow up work could focus on testing the optimal range for tunnel oxide thickness in TOPCon solar cells, as well as improving the manufacturing process to produce better control of film thickness. This work could be extended into more advanced TOPCon solar cells including double or triple stack structures, as well as experimental pinhole designs.

SOLAR ENERGY↗

DOE OSTI2025

Flat nonlinear optics with intersubband polaritonic metasurfaces

Nonlinear intersubband polaritonic metasurfaces produce some of the strongest second- and third-order nonlinear optical responses reported for condensed matter systems at infrared frequencies. These metasurfaces are fabricated as two-dimensional arrays of nanoresonators from multi-quantum-well semiconductor heterostructures, designed to produce strong nonlinear responses associated with intersubband transitions. By optimally coupling the optical modes of the nanoresonators to vertically polarized intersubband transitions in semiconductor heterostructures, one can boost the nonlinear response associated with intersubband transitions, make intersubband transitions interact with free-space radiation at normal incidence, and hence produce optically thin flat nonlinear optical elements compatible with free-space optical setups. As a result of the strong nonlinear response in these systems, significant nonlinear conversion efficiencies (>0.1 %) can be attained in deeply subwavelength optical films using modest pumping intensities of only 10–100 kW/cm 2 . Subwavelength metasurface thickness relaxes phase-matching constraints limiting the operation of bulk nonlinear crystals. Furthermore, the amplitude and phase of the nonlinear optical response in intersubband polaritonic metasurfaces can be tailored for a specific pump wavelength and a nonlinear process of interest through the co-optimization of quantum engineering of electron states in semiconductor heterostructures and photonic engineering of the metasurface nanoresonators design. Additionally, an applied voltage can dynamically control the amplitude and phase of the nonlinear optical response at a nanoresonator level. Here, we review the current state of the art in this rapidly expanding field, focusing on nonlinear processes supporting second-harmonic generation, saturable absorption, and optical power limiting.

flat optics↗

DOE OSTI2024

Robust Data-Driven Predictive Run-to-Run Control for Automated Serial Sectioning

This letter presents a one-step predictive run-to-run controller (R2R-MPC) for the automation of mechanical serial sectioning (MSS), a destructive material analysis process. To address the inherent uncertainty and disturbances in the MSS process, a robust closed-loop approach is presented. Here, the robust R2R-MPC models the uncertainty of the MSS process using a linear differential inclusion. As an analytical model of the MSS process is unavailable, the differential inclusion is identified from historical data. The R2R-MPC is posed as an optimization problem that computes incremental changes to the control input which minimize the worst-case material removal errors. This optimization-based controller is combined with a run-to-run controller to provide integral action that rejects constant disturbances and tracks constant reference removal rates. To demonstrate the efficacy of our robust R2R-MPC, we present simulation results which compare the presented controller with a conventional non-robust R2R.

42 ENGINEERING↗

DOE OSTI2026

Diabatization with Electrostatic Embedding for Studying Photophysics in Organic Molecular Crystals

Highly emissive organic molecular crystals find applications in several areas, such as organic electronics, solar cells, and sensors. Understanding the excited-state mechanisms underlying these applications is essential for optimizing and controlling them effectively. Exciton models coupled with nonadiabatic dynamics, particularly quantum dynamics, provide crucial insights into photochemical and photophysical processes in molecular crystals. Nevertheless, there remains a lack of general tools and automated workflows to facilitate such simulations. In this paper, we present a computational strategy to investigate the photoactivated dynamics of organic molecular crystals, bridging methodologies traditionally used for molecular systems and materials science, with a particular focus on the interplay between local excitations and charge transfer (CT) processes. We have implemented an interface between the fromage and Overdia programs, enabling the construction of vibronic Hamiltonians for molecular crystals within an excited-state ONIOM(QM:QM′) framework, incorporating long-range electrostatics through a RESP-based Ewald summation. Fragment-based diabatization provides a route to quantum dynamics simulations in weak-to-intermediate coupling regimes. The method was applied to the photophysics of dibenzo[g,p]chrysene (DBC) crystals using time-dependent DFT. The fromage/ Overdia interface was employed to compute the couplings of local excitations and CT states for 18 unique DBC dimers in the crystal and to quantify the influence of electrostatic embedding, which was found to be modest (10−20%). Simulations on π-stacked dimers reproduced the small red shift observed experimentally from solution to crystal, attributed to electronic interactions among fixed monomers rather than crystal electrostatics. Quantum dynamics simulations revealed ultrafast population transfer from bright local excitations to CT states. This approach establishes a robust framework linking molecular and solid-state excited-state dynamics, with potential applications for studying excitations, defects, and impurities in molecular crystals.

Crystals↗

DOE OSTI2024

Atomic Precision Processing of Two-Dimensional Materials for Next-Generation Microelectronics

The growth of the information era economy is driving the pursuit of advanced materials for microelectronics, spurred by exploration into “Beyond CMOS” and “More than Moore” paradigms. Atomically thin 2D materials, such as transition metal dichalcogenides (TMDCs), show great potential for next-generation microelectronics due to their properties and defect engineering capabilities. This perspective delves into atomic precision processing (APP) techniques like atomic layer deposition (ALD), epitaxy, atomic layer etching (ALE), and atomic precision advanced manufacturing (APAM) for the fabrication and modification of 2D materials, essential for future semiconductor devices. Additive APP methods like ALD and epitaxy provide precise control over composition, crystallinity, and thickness at the atomic scale, facilitating high-performance device integration. Subtractive APP techniques, such as ALE, focus on atomic-scale etching control for 2D material functionality and manufacturing. In APAM, modification techniques aim at atomic-scale defect control, offering tailored device functions and improved performance. Achieving optimal performance and energy efficiency in 2D material-based microelectronics requires a comprehensive approach encompassing fundamental understanding, process modeling, and high-throughput metrology. Finally, the outlook for APP in 2D materials is promising, with ongoing developments poised to impact manufacturing and fundamental materials science. Integration with advanced metrology and codesign frameworks will accelerate the realization of next-generation microelectronics enabled by 2D materials.

36 MATERIALS SCIENCE↗