Results for “optimal transport” — Page 2

NASA NTRS2026

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↗

NASA NTRS2026

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↗

NASA NTRSDate not supplied

Design and Optimization of an Open Rotor Stage

Open rotor propulsors enable greatly increased effective bypass ratios over current aircraft engines, resulting in significantly improved efficiencies. One of the most promising of these geometries is the “open fan”, which consists of a single rotor followed by a single stator. This configuration will be referred to as an “open rotor stage” for design work at NASA. In this work we consider the multidisciplinary design and optimization of an open rotor stage using a variety of 2D and 3D techniques. The 2D aerodynamic techniques used for design include a 2D Trefftz plane method, axisymmetric throughflow, and quasi-3D blade-to-blade CFD. Aerodynamic performance is prioritized, while structural requirements are met and some acoustic considerations are included. Between the rotor, stator, and hub profiles, a total of 101 design variables are used. Differences in the process of designing an open rotor stage vs. designing other high-speed propellers are also discussed. The current design, the NASA Optimized Propulsor for Efficiency and Noise (N-OPEN-1), achieves 84.7% net efficiency as predicted by 3D CFD, with further improvements expected to be possible. This design makes effective use of a slower rotor and a less tip-loaded work distribution than typical single-rotation propellers, allowing the stator to produce 14.8% of the thrust. Laminar flow on the blades was responsible for a 1.7% efficiency benefit and warrants further study.

Open rotors↗

NASA NTRS2026

Design and Optimization of an Open Rotor Stage

Open rotor propulsors enable greatly increased effective bypass ratios over current aircraft engines, resulting in significantly improved efficiencies. One of the most promising of these geometries is the “open fan”, which consists of a single rotor followed by a single stator. This configuration will be referred to as an “open rotor stage” for design work at NASA. In this work we consider the multidisciplinary design and optimization of an open rotor stage using a variety of 2D and 3D techniques. The 2D aerodynamic techniques used for design include a 2D Trefftz plane method, axisymmetric throughflow, and quasi-3D blade-to-blade CFD. Aerodynamic performance is prioritized, while structural requirements are met and some acoustic considerations are included. Between the rotor, stator, and hub profiles, a total of 101 design variables are used. Differences in the process of designing an open rotor stage vs. designing other high-speed propellers are also discussed. The current design, the NASA Optimized Propulsor for Efficiency and Noise (N-OPEN-1), achieves 84.7% net efficiency as predicted by 3D CFD, with further improvements expected to be possible. This design makes effective use of a slower rotor and a less tip-loaded work distribution than typical single-rotation propellers, allowing the stator to produce 14.8% of the thrust. Laminar flow on the blades was responsible for a 1.7% efficiency benefit and warrants further study.

turbomachinery↗

NASA NTRSDate not supplied

Aerothermal Shape Optimization of Actively-Cooled Battery Packs Using Conjugate Heat Transfer

Thermal management for battery is important for electric aircraft because battery temperature is critically important to vehicle safety, and it also has direct impact on the efficiency of the battery system. Because ambient air is a readily available resource for aircraft, this paper considers an active cooling concept with forced convection of ambient air through the battery pack. Conjugate heat transfer analysis is used to solve the coupled aero-thermal problem, which consists of a finite-volume computational fluid dynamics solver for the fluid domain, and a conduction heat transfer solver for the solid domain. A mixed Neumann and Dirichlet boundary condition is developed for the fluid-solid interface, which allows the solid domain to completely submerge in the fluid domain. A gradient-based optimization method is adopted, and the discrete adjoint approach implemented in DAFoam is used to efficiently compute the gradients. The aero-thermal coupling for primal analysis and gradient computation is handled using the OpenMDAO-based MPhys framework. A constant heat source is prescribed for the battery cells, and the battery shape (design variable) is optimized to minimize cooling pump power and battery weight (composite objective function) while keeping the battery temperature below a threshold (constraint). The optimized design achieves a 44.6% and 1.5% reduction in the cooling pump power and battery weight, respectively, and the maximal temperature constraint is satisfied. This work has the potential to reduce battery-pack weight, improve performance, and reduce the weight of thermal management systems for electric vertical take-off and landing aircraft.

heat transfer↗

NASA NTRSDate not supplied

Aerothermal Shape Optimization of Actively-Cooled Battery Packs using Conjugate Heat Transfer

Thermal management for battery is important for electric aircraft because battery temperature is critically important to vehicle safety, and it also has direct impact on the efficiency of the battery system. Because ambient air is a readily available resource for aircraft, this paper considers an active cooling concept with forced convection of ambient air through the battery pack. Conjugate heat transfer analysis is used to solve the coupled aero-thermal problem, which consists of a finite-volume computational fluid dynamics solver for the fluid domain, and a conduction heat transfer solver for the solid domain. A mixed Neumann and Dirichlet boundary condition is developed for the fluid-solid interface, which allows the solid domain to completely submerge in the fluid domain. A gradient-based optimization method is adopted, and the discrete adjoint approach implemented in DAFoam is used to efficiently compute the gradients. The aero-thermal coupling for primal analysis and gradient computation is handled using the OpenMDAO-based MPhys framework. A constant heat source is prescribed for the battery cells, and the battery shape (design variable) is optimized to minimize cooling pump power and battery weight (composite objective function) while keeping the battery temperature below a threshold (constraint). The optimized design achieves a 44.6% and 1.5% reduction in the cooling pump power and battery weight, respectively, and the maximal temperature constraint is satisfied. This work has the potential to reduce battery-pack weight, improve performance, and reduce the weight of thermal management systems for electric vertical take-off and landing aircraft.

thermal management↗

NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗

NASA NTRS1993

Center for Space Transportation and Applied Research Fifth Annual Technical Symposium Proceedings

This Fifth Annual Technical Symposium, sponsored by the UT-Calspan Center for Space Transportation and Applied Research (CSTAR), is organized to provide an overview of the technical accomplishments of the Center's five Research and Technology focus areas during the past year. These areas include chemical propulsion, electric propulsion, commerical space transportation, computational methods, and laser materials processing. Papers in the area of artificial intelligence/expert systems are also presented.

Source record↗

NASA NTRS2026

Moon Base Transportation - Deliveries to the Lunar Surface

Explore the source record for details and available documents.

Lunar Lander↗

NASA NTRS2026

Optimization of a 10–Step Reduced CH₄/O₂ Combustion Mechanism for RDRE Applications

A 10-step, 9-species reduced mechanism for methane/oxygen combustion is evaluated and optimized for use in rotating detonation rocket engine (RDRE) simulations at an operating pressure of 10 bar. The mechanism is assessed against GRI-Mech 3.0 for ignition delay times, adiabatic equilibrium temperature and species, Chapman-Jouguet (CJ) detonation parameters, and laminar flame speed. A sequential Nelder–Mead optimization procedure is applied to the rate constants of reactions R1, R4, and R5, targeting ignition delay agreement over the equivalence ratio range φ = 1.0–1.4 and temperature range T = 1400–2000 K. The optimized mechanism achieves ignition delay agreement within 28% of GRI-Mech 3.0 for the temperature range relevant to RDRE operation (τ ≥ 5 μs). Adiabatic equilibrium temperatures and CJ detonation parameters are reproduced within 0.15% of GRI-Mech 3.0 values, confirming thermodynamic consistency of the mechanism. Laminar flame speed predictions deviate significantly from GRI-Mech 3.0, which is attributed to the absence of HO₂ chemistry in the reduced mechanism; this limitation does not affect RDRE simulation fidelity since laminar flame propagation is of secondary importance in RDREs.

Computational Fluid Dynamics↗

NASA NTRS2003

Optimal Battery Charging for Damage Mitigation

Our control philosophy is to charge the NiH2 cell in such a way that the damage incurred during the charging period is minimized, thus extending its cycle life. This requires nonlinear dynamic model of NiH2 cell and a damage rate model. We must do this first. This control philosophy is generally considered damage mitigating control or life-extending control. This presentation covers how NiH2 cells function, electrode behavior, an essentialized model, damage mechanisms for NiH2 batteries, battery continuum damage modeling, and battery life models. The presentation includes graphs and a chart illustrating how charging a NiH2 battery with different voltages and currents affects damages the battery and affects its life. The presentation concludes with diagrams of control system architectures for tracking battery recharging.

Hartley, Tom T.↗

NASA NTRS1992

Nickel-hydrogen battery design for the Transporter Energy Storage Subsystem (TESS)

Information is given in viewgraph form on nickel hydrogen battery design for the transporter energy storage subsystem (TESS). Information is given on use in the Space Station Freedom, the launch configuration, use in the Mobile Servicing Center, battery design requirements, TESS subassembley design, proof of principle testing of a 6-cell battery, possible downsizing of TESS to support the Mobile Rocket Servicer Base System (MBS) redesign, TESS output capacity, and cell testing.

Lapinski, John R.↗

NASA NTRS1958

Turbojet Lift Engine Design for Vtol Transports

The pure hoverjet, utilizing separate turbojet engines for lift and cruise phases of flight, is an interesting high speed VTOL transport airplane configuration. The results of a brief analysis are presented with the intent to: (I) define the effects of some general powerplant installation and operating variables on VTOL transport performance; (2) indicate desirable turbojet lift engine design characteristics with respect to compressor pressure ratio, turbine inlet temperature, component efficiencies and thrust augmentation; and (3) examine the unique lift engine operating requirements for feasible ways of reducing engine specific weight. The figure of merit used to gauge airplane performance is airplane range.

M J Saari↗

NASA NTRS2026

Mathematical Model of a Regenerative Fuel Cell for System Optimization

This thesis developed a system-level optimization model of a regenerative fuel cell (RFC) system for long-duration, off-world energy storage applications. Prior RFC design studies have typically been limited to reduced parameter sets and simplified constraints due to computational limitations relative to the number of relevant degrees of freedom. As a result, important nonlinear interactions between subsystems have not been fully captured. This work began to address that gap by developing a higher-fidelity, nonlinear optimization framework that incorporates a broader set of design variables and coupled constraints, enabling a multidimensional model that captures the coupled behavior of RFC subsystems and demonstrates the feasibility of applying optimization to such systems. An expanded system-level optimization approach was established that captures interactions between electrochemical performance, structural requirements, and storage design. This enabled a more comprehensive evaluation of trade-offs than conventional formulations. The model integrates four coupled subsystems: a fuel cell, an electrolyzer, reactant gas, and high-pressure storage tanks, and was formulated to accommodate a wide range of mission parameters, including operational time and required output power. It incorporates constraints on available solar array power, reactant mass balance between production and consumption, and pressure-dependent storage requirements. To enable reliable convergence, the optimization problem was reformulated to reduce dimensionality and improve numerical stability, with subsystem models organized for efficient evaluation. Problem dimensionality was reduced by consolidating lower-level design variables into higher-level representative quantities, and subsystem behavior was evaluated within the optimization loop. A multi-start initialization strategy was employed to mitigate sensitivity to local minima and improve solution quality, while nonlinear relationships were solved using robust numerical methods. The results showed that convergence was achieved across a range of required output power values. Specific energy reached a maximum at a critical mission power level, where the electrolyzer power matched the available solar input and operated near its voltage and current density limits. Beyond this point, further increases in required power resulted in less mass-efficient operation, increasing total system mass and reducing overall performance. The developed model represents an advancement in RFC system-level optimization by enabling analysis of a broader and more tightly coupled design space than previous considerations. While convergence behavior and computational cost remain challenges, the methods introduced improve solvability and allow inclusion of additional design variables with minimal loss of physical fidelity. However, the numerical results should not be interpreted as definitive design recommendations, as the model includes simplifying assumptions and omits several higher-order effects. Future work should extend this framework by incorporating additional subsystems and loss mechanisms, such as thermal management, parasitic power consumption, and reactant losses, to improve fidelity and ensure more representative design conclusions.

Electrochemistry↗

NASA NTRS1994

Vapor Transport Crystal Growth of Mercury-Cadmium-Telluride in Microgravity

Two epitaxial growth experiments of Hg 1-x Cd x Te layers on (100) CdTe substrates in closed ampoules using HgI 2 as a transport agent have been performed during the USML-1 mission. The characterization results to date demonstrate a considerable improvement of the space-grown epitaxial layers relative to ground-control specimens in terms of morphology, compositional uniformity, and structural micro homogeneity. These results show the effects of microgravity and fluid dynamic disturbances on-ground on the deposition and growth processes. The continued analysis of this technologically important system is designed to further elucidate the observed crystallographic improvements and their relation to mass flow.

Heribert Wiedemeier↗

NASA NTRSDate not supplied

Mission Design Considerations for a Low-Thrust Spacecraft

Developing an executable low-thrust trajectory for use in a spaceflight mission requires the design and optimization of a deterministic trajectory as well as the validation that the selected architecture is robust to some set of uncertainties, execution errors, and potential contingencies. Uncertainty in the ability of the spacecraft and its launch vehicle to execute a trajectory as well as potential deviations such as in-flight anomalies combine with design-to constraints and requirements to complicate the optimization problem. The approach by which robust mission design was accomplished for the initial capability of NASA’s Gateway is presented as well as associated results.

NRHO↗

NASA NTRSDate not supplied

Mission Design Considerations for a Low-Thrust Spacecraft

Developing an executable low-thrust trajectory for use in a spaceflight mission requires the design and optimization of a deterministic trajectory as well as the validation that the selected architecture is robust to some set of uncertainties, execution errors, and potential contingencies. Uncertainty in the ability of the spacecraft and its launch vehicle to execute a trajectory as well as potential deviations such as in-flight anomalies combine with design-to constraints and requirements to complicate the optimization problem. The approach by which robust mission design was accomplished for the initial capability of NASA’s Gateway is presented as well as associated results.

mission design↗

NASA NTRSDate not supplied

Helmholtz Motor: A Novel Electric Machine That Enables High Temperature Superconducting Armatures

Electrified aircraft are being developed to increase the efficiency and reduce the cost of operating subsonic transport aircraft. Achieving a substantial impact necessitates focusing on single- and twin-aisle aircraft which use propulsion systems with >20 MW ratings. For these aircraft, multi-MW superconducting electric machines are being developed due to their high specific power and high efficiency. Cryogenic electric machines are a promising technology for multi megawatt electric aircraft drivetrains. Second generation high temperature superconductors (HTS) are the ideal conductor for the field winding (typically the rotor) of superconducting machines, and they have attractive features for the armature winding (typically the stator) of these machines. However, their use in armature windings has been severely limited due to excessive AC losses in conventional electric machines. This presentation will present the working principle, design, and a performance trade study of a novel electric machine – termed a Helmholtz machine – that enables HTS armatures by significantly reducing AC losses. The working principle of the patent pending Helmholtz motor is to optimize the rotor-produced magnetic field (i.e., the field winding) so that it produces a magnetic field almost purely in the plane of the HTS. This is accomplished using 2 rotors that contain a set of matched Helmholtz coil pairs (or matched permanent magnet Halbach arrays). The armature (stator) HTS coils are positioned between the rotors and oriented to minimize the out of plane magnetic field. The armature HTS coils are also designed to minimize the out of plane component of their self field. The presentation will include the lessons learned in the sizing and design of this motor, including equations for how to match the magnet arrays. A performance trade study will be presented for a partially superconducting, radial flux version of the motor. The trade study involved an optimization of a motor design code based on analytical calculations (electromagnetic, AC loss, thermofluid, mechanical, and motor sizing) that determine the motor’s total efficiency and total specific power. The design code will be described in the presentation, with an emphasis on the calculation of magnetic fields, AC loss, and temperature distribution.

Superconducting electric machines↗