AI-Enhanced System Level Design and Optimization of Inertial Fusion Energy (IFE) Power Plants using the Integrated Process Model (IPM)
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Nuclear science and engineering↗
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Results for “Trajectory Optimization”
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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Spacecraft Antenna Pointing
In examining the characteristic inaccuracies of a pre-programmed high-gain antenna pointing system, the effects of spacecraft attitude errors, as embodied in pitch, yaw and roll limit cycle activity, are of major concern. If the pointing accuracy requirements for a particular mission allow the use of a single degree of freedom pointing system, it is doubtful whether a method of correcting antenna position for spacecraft attitude errors would be worthwhile. This is because the pointing error due to fitting a plane curve to a three-dimensional trajectory would probably be at least as large as the additional pointing errors due to attitude drift within the deadbands. Further, trying to correct pointing for pitch, yaw, and roll drifts by adjusting a single hinge angle is relatively ineffective.
ERROR SIGNAL↗
Feasibility Study of a Carbon Dioxide Breathing, Solid-Fueled Ramjet for Martian Descent and Landing of Human Class Payloads
This study analyzes the cross-range capabilities of solid-fueled ramjets in a Martian atmosphere for applications in entry, descent, and landing. Trajectories and engine operability are characterized using Mach and dynamic pressure operational points. It is predicted that use of atmosphere-breathing propulsion will enable access to additional surface latitudes without the need for orbital plane changes. Development of an integrated design environment facilitates preliminary ramjet design and analysis, and enables sensitivity studies to identify high impact areas of technology development. In addition, additive manufacturing supports prototyping, rapid ground testing, and allows for utilization of complex geometries.
Shi Broadwell↗
Deciphering the Crystallinity-Dependent Sensitivity of Charge Injection in n-Channel Organic Transistors: Reliable Characterization and Optimized Performance
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36 MATERIALS SCIENCE↗
Multidisciplinary Design Optimization and Analysis of an Open Rotor Stage: Part 1
Successful design of open rotor propulsors requires effective analysis across multiple disciplines, including aerodynamics, acoustics, and structures. A viable design must not only be efficient but must also produce an acceptable level of noise and meet all static and dynamic structural requirements. For design and optimization, this is especially challenging because running high fidelity analyses is resource-intensive, and optimizing a design may require many thousands of cases to be analyzed. For this reason, the NASA team has applied design methodology that utilizes low-cost aerodynamic methods, machine learning models, and high-fidelity analyses when necessary. This includes standard two-dimensional methods such as throughflow analysis and quasi-3D blade-to-blade CFD, as well as some newly developed methods. Optimization using 3D CFD is necessary to maximize performance, and this is considered as well. All optimizations are carried out subject to structural constraints evaluated using finite element analysis. Doing this accurately requires a robust trunnion design, capable of pitching the blade between cruise and takeoff conditions while maintaining acceptable factor of safety. Hot to cold analysis must also be applied in order to correctly determine the as-manufactured shape. For acoustics, the unsteady pressures on the blade surfaces must be predicted, and this can be done either through full-annulus unsteady CFD or through a nonlinear harmonic method (NLH). NLH can run much faster, allowing some acoustic considerations to be made earlier in the design process. The design process is ongoing, and this presentation will review the current status and planned next steps. This part of the talk will focus on aerodynamic performance and be followed by a talk on structures and acoustics.
Design↗
Optimal experimental design using eigenvalue-based criteria with Pyomo.DoE
New developments in automated optimal experimental design within the PSE+ software ecosystem. Advancements in user experience (to reduce the time taken to perform optimal experiment design) and computational capabilities (allowing more diverse experimental design) are shown with an example relevant to critical minerals and materials. Also, a small tutorial on science-based optimal experimental design and novel contributions therein are presented.
97 MATHEMATICS AND COMPUTING↗
PAL 2.0: a physics-driven bayesian optimization framework for material discovery
PAL 2.0 provides an efficient discovery tool for advanced functional materials, ameliorating a major bottleneck to enabling advances in next-generation energy, health, and sustainability technologies.
Chemistry↗
Magnet System Design and Optimization for a Novel Plasmoid Magnetic Reconnection Thruster Prototype
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Wang, Jiawen [Princeton Plasma Physics Laboratory ↗
Robust optimization of flexible diafiltration systems for critical mineral separations
This paper provides major contributions in expanding the literature for membrane process design with critical mineral recovery applications and showcasing the importance of robust design techniques for reducing risks of underperformance in such systems. Here, a membrane process flowsheet featuring PrOMMiS membrane models for recovering lithium/cobalt from spent batteries is showcased, uncertainty in membrane sieving and localized fouling are considered, and robust designs are obtained using the PyROS toolset. This paper is intended for a general audience of researchers working in critical minerals, membranes, and optimization related areas.
36 MATERIALS SCIENCE↗
NASA battery testbed: A designed experiment for the optimization of LEO battery operational parameters
Simulation of spacecraft battery operation is implemented. Robust design experiment to obtain optimum battery operational parameters is performed. It is found that short term tests using robust design of experiments can provide guidelines for optimum battery operation. It is decided to use robust design approach to provide guidelines for battery operation on current spacecraft in orbit as batteries age (GRO, UARS, EUVE, TOPEX).
Deligiannis, F.↗
Security Constrained Economic Optimization of Photovoltaic and Other Distributed Assets
The rapid growth of distributed energy resources (DERs), especially photovoltaic (PV) systems, has introduced new complexities in maintaining grid reliability, stability, and cost-effective operation. This project addresses these challenges by developing and demonstrating a scalable GridOS Distributed Energy Resource Management System (DERMS) that enables secure, real-time optimization and control of DERs at the distribution feeder level.
14 SOLAR ENERGY↗
Refractory-based thermal energy storage for industrial process heat: one-dimensional modeling, control, and optimization
The variable and weather-dependent output of wind and solar power plants present a substantial challenge for planning and operating electricity-systems, particularly in the absence of cost-effective and dispatchable energy storage technologies. This study investigates a high-temperature, electrically heated, refractory-based thermal energy storage (RTES) system that stores electrical energy as sensible heat in dense ceramic bricks over the 950–1800 °C range. The stored heat can be discharged as a controlled hot-gas stream for industrial heating, fuel substitution in high-temperature processes, or electricity generation. The main novelty is a comprehensive modelling, control, mapping, and optimization framework that integrates one-dimensional transient gas–solid heat transfer, fan-assisted discharge, bypass-flow regulation, reheating logic, fan-power evaluation, insulation-loss assessment, and genetic-algorithm-based design optimization. The model uses feedback from outlet temperature and delivered power to regulate discharge, while a two-stage genetic algorithm optimizes brick-channel geometry, gas-flow operation, and multilayer insulation thicknesses. Storage capacities below 50 MWh and discharge powers of 5–30 MW are analyzed to evaluate hold time, thermal delivery, fan-power penalty, heat loss, state-of-charge evolution, and indicative capital cost. Results demonstrate that optimized and well-insulated refractory-based thermal energy storage units can provide stable, efficient, and repeatable heat delivery over multiple discharge cycles. The generated performance and cost maps support modular refractory thermal energy storage as a practical option for large-scale integration of wind and solar generation and for high-temperature industrial process heat.
25 ENERGY STORAGE↗
Development of a Thermal Radiator Optimization Tool with Alternate Coolants
The development of a modeling tool to optimize the design of a thermal radiator based on capacity, rejection temperature, geometry, type and coolant is presented. The radiator size is determined from the desired capacity, environmental conditions and rejection temperature. Flow through the radiator is derived from the overall heat load and prescribed inlet to outlet delta temperature for a given coolant. The tool considers radiator geometry (i.e., tube spacing and diameter, face-sheet thickness, overall size, etc.) and general type (i.e., parallel/manifold versus serpentine) in the optimization. Selection from a handful of potential coolants is also available in the tool which primarily affects necessary tube diameter to maintain turbulent flow and minimize pressure drop. An Equivalent System Mass (ESM) approach is utilized to include pump power in the optimization. Results from the tool and potential future enhancements are also discussed.
Coolant↗
Active learning using hybrid surrogate tool life modeling for machining process optimization
Here, this paper describes an active learning approach for part-to-part iterative machining process optimization using a hybrid surrogate tool life model. A probabilistic interpolating tool life model is developed by combining the empirical Taylor-type tool life equation and the model fit error. The probabilistic tool life model is then used to calculate the machining cost per part distribution. The optimal machining parameters are selected using an expected improvement in machining cost per part criterion. The method is validated numerically using experimental results; the results show a median convergence error of 2.2% after three tests over 400 simulations. The method is validated experimentally on two industrial applications for Ti-6Al-4V roughing resulting in a cost per part reduction greater than 23% after two tests. The described method is a robust solution for rapid convergence to optimal machining parameters in an industrial production environment.
Active learning↗
A Modular Conjugate Heat Transfer Optimization Framework for Thermal Management of Electric Aircraft
Conjugate heat transfer (CHT) analysis and optimization is a powerful method for improving thermal management, as it simultaneously resolves the temperature distribution in both fluid and solid domains. This paper presents a modular, discrete adjoint-based CHT optimization capability integrated within the OpenMDAO/MPhys framework. A unique feature of the proposed framework is its flexibility to extend to multidisciplinary optimization, including aero-structural-thermal applications. The fluid domain is modeled using a finite-volume Computational Fluid Dynamics (CFD) solver, and the solid domain with a conduction heat transfer solver. A mixed Neumann-Dirichlet boundary condition is developed to enable full submersion of the solid geometry within the fluid domain, while ensuring consistent temperature and heat flux coupling at the CHT interface. Gradient-based optimization is performed; the gradients are efficiently computed using the discrete adjoint solvers implemented in DAFoam. To demonstrate the method, this paper considers two cases related to electric aircraft thermal management: a U-bend heat exchanger and an actively cooled battery pack. The U-bend case aims to minimize pressure loss while maximizing heat flux by changing the pipe geometry. The optimized design reduces pressure loss by 52.7% and increases total heat flux by 2.3%. In the battery pack case, a 3-by-3 cell configuration is cooled by ambient airflow, with constant heat generation prescribed in the cells. The battery casing shape serves as the design variable, and the objective function is a weighted sum of pressure loss and pack weight, subject to a maximum temperature constraint. The optimized design achieves a 44.6% reduction in pressure loss and a 1.5% reduction in weight, while satisfying the thermal constraint. To ensure the reliability of the optimized designs, this study validates coarse-mesh, steady-state predictions against fine-mesh unsteady simulations, demonstrating consistency within acceptable errors. This work demonstrates the potential of the developed framework to enable rapid, high-fidelity design of thermal management systems for electric aircraft.
heat transfer↗
Smart Charging of Fleet and Personal Electric Vehicles through Joint Vehicle-to-Grid Optimization
As electric vehicle (EV) adoption accelerates, vehicle-to-grid (V2G) strategies offer advantages over unmanaged charging (V0G) by enhancing grid stability, reducing fleet operation costs, and supporting integration of variable generation resources. This research develops a day-ahead optimization framework linked with agent-based simulations to evaluate coordinated V2G participation by fleet and personal EVs under 5 energy-pricing settings in Austin, Texas. Three scenarios (V0G, fleet-only V2G, and joint-V2G) are examined, considering real-time price and grid profiles, health-damage costs, and operational constraints for both fleet and personal EVs. Results show how V2G scenarios shift fleet EV charging to mid-day while enabling strategic battery-discharge during evening peaks, mitigating grid stress and lowering EV energy costs. V2G delivers close to 80% energy-cost savings for a 2000-EV fleet in Austin on grid-stressed days, with 55% lower charging pollutant outputs. Joint-V2G amplifies system-level benefits by complementing fleet discharge, but smart-charging equipment costs can offset those benefits.
Electric vehicle↗
Optimizing Renewable Ammonia Production for a Sustainable Fertilizer Supply Chain Transition
Local renewable ammonia production using electrolytic hydrogen is an emerging approach to alleviate emissions attributed to synthetic nitrogen fertilizer production while also insulating against fluctuations in fertilizer prices and mitigating transportation costs and emissions. However, replacing ammonia currently produced using fossil fuels will not be immediate. To this end, we develop a supply chain transition model, which first optimizes the design and hourly operation of new renewable ammonia facilities to minimize production costs and then optimizes the annual installation timing, production scale, and location of these new renewable facilities along with ammonia transportation to meet county resolution demands. The objective is to augment and eventually replace conventional ammonia market imports in an economically competitive manner. We performed a case study for Minnesota's ammonia supply chain and found that a full transition to in-state renewable production by 2032 is optimal. This is incentivized by the U.S. federal government's clean hydrogen production credits. This transition results in 99 % reduction in carbon intensity along with stable supply costs below $475 per metric tonne. New renewable production facilities are an order of magnitude smaller than existing conventional plants. They use both wind and solar resources and operate dynamically to minimize expensive battery and hydrogen storage capacities.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Benchmarking Bayesian Optimization Frameworks and Acquisition Strategies for Materials Discovery and Autonomous Laboratories
Bayesian optimization (BO) can accelerate materials discovery by guiding expensive experiments toward the most promising processing conditions. We systematically compare five BO surrogate and framework combinations (Gaussian processes in Ax, Gaussian processes and Monte-Carlo neural networks in BayBE, random forests in Lolopy, and tree-structured Parzen (TPE) estimators in Hyperopt) on three benchmarks that mimic common materials design tasks (a discrete solid-electrolyte composition space, a hybrid discrete/continuous laminate-composite design problem solved with micromechanics modeling, and the continuous Ishigami analytic function which is a standard optimization benchmark). Each BO surrogate is paired with posterior mean, probability of improvement, and expected improvement acquisition functions and run for 100 trials from randomized initial samples with uniform random search providing a control. Across five random seeds per setting, BayBE’s Gaussian-process surrogate with expected improvement consistently reached ≥95 % of the known optimum in the fewest evaluations, while Lolopy’s random forest matched or exceeded GP performance on purely categorical or mixed spaces at a higher computational cost. Posterior mean alone often stagnated at local optima, underscoring the need for exploration, whereas probability and expected improvement balanced exploration and exploitation leading to better optimization in fewer trials. Execution times ranged from milliseconds for TPE to minutes for neural-network and random-forest surrogates. These results establish baseline expectations for BO in automated materials laboratories and highlight expected improvement with Gaussian processes as a reliable first choice, with random forests offering a strong alternative when categorical variables dominate. The benchmark suite and code are released to facilitate future surrogate, acquisition, and constraint-handling research in data-driven materials optimization.
Bayesian optimization↗