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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Integrated Turbine Component Cooling Designs Facilitated by Additive Manufacturing and Optimization

This research program involved the development of improved gas turbine cooling by optimizing the combined internal and film cooling configurations used for turbine blades and vanes. Improved cooling of turbine airfoils allows the gas turbines to be operated at higher temperatures which improves efficiency for gas turbine-based power generation. This also facilitates operation with hydrogen-based fuels which inherently generate higher operating temperatures. Designs of the new cooling configurations were based on the added geometric flexibility that is available when using metal additive manufacturing techniques. When making these designs, adjustments were made to account for imperfections occurring in the metal additive manufacturing process. New designs for film cooling holes were developed using computational adjoint gradient optimization techniques which generated a unique film cooling hole with 70% greater film cooling effectiveness compared to conventional shaped film cooling holes. A major factor in improving the film cooling effectiveness were small external protrusions on either side of the new hole which generated vortices that pushed coolant towards the surface and increased the lateral spreading of the coolant. Our studies also included optimization of film cooling holes that can be built with conventional manufacturing techniques and optimizing the internal coolant channels that supply coolant to the film cooling holes. Various internal coolant channel shapes were investigated and designs that maximized the cooling of the airfoil while minimizing pressure losses in the channel flows were identified. Testing and verification of the performance of the final configurations was done with a transonic wind tunnel facility which allowed testing at engine realistic high velocities. Final testing of the combined internal and film cooling configurations was done by incorporating the configurations in Penn State’s National Experimental Turbine (NExT) vane. These tests confirmed the enhanced overall cooling effectiveness provided by the new cooling configuration designs.

03 NATURAL GAS↗
NASA NTRS1972

Optimized space shuttle trajectory simulation

A computer program for simulating optimized space shuttle trajectories is presented. The program considers the ascent, return, and synergetic maneuvers of lifting boosters and spacecraft. Other constraints on optimum trajectory such as maximum heating, acceleration, angle of attack, and excluded or included overflight or impact regions are examined. Mathematical models are included to support the theoretical aspects of the discussion.

Tramonti, L.↗
NASA NTRS1971

Methodologies for optimal resource allocation to the national space program and new space utilizations. Volume 1: Technical description

The optimal allocation of resources to the national space program over an extended time period requires the solution of a large combinatorial problem in which the program elements are interdependent. The computer model uses an accelerated search technique to solve this problem. The model contains a large number of options selectable by the user to provide flexible input and a broad range of output for use in sensitivity analyses of all entering elements. Examples of these options are budget smoothing under varied appropriation levels, entry of inflation and discount effects, and probabilistic output which provides quantified degrees of certainty that program costs will remain within planned budget. Criteria and related analytic procedures were established for identifying potential new space program directions. Used in combination with the optimal resource allocation model, new space applications can be analyzed in realistic perspective, including the advantage gain from existing space program plant and on-going programs such as the space transportation system.

Source record↗
DOE OSTI2025

Multistage economic MPC for systems with a cyclic steady state: A gas network case study

Multistage model predictive control (MPC) provides a robust control strategy for dynamic systems with uncertainties and a setpoint tracking objective. Moreover, extending MPC to minimize an economic cost instead of tracking a pre-calculated optimal setpoint improves controller performance. This paper presents a novel multistage economic nonlinear model predictive control (E-NMPC) framework for dynamic systems operating under uncertainty, with specific application to natural gas transmission networks. A key innovation lies in the integration of cyclic steady-state (CSS) constraints within the multistage MPC formulation, enabling the controller to manage periodic operating conditions commonly observed in energy systems. A Lyapunov-based descent condition is enforced to ensure robust stability of the controller. The multistage economic MPC framework is validated on two gas pipeline case studies, where it successfully minimizes net energy consumption, respects operational constraints under uncertain demand profiles, and guides the network to optimal cyclic operation. The Lyapunov function remains bounded in both case studies, validating the robust stability of multistage E-NMPC.

03 NATURAL GAS↗
DOE OSTI2019

Managing uncertainty in data-driven simulation-based optimization

Optimization using data from complex simulations has become an attractive decision-making option, due to ability to embed high-fidelity, non-linear understanding of processes within the search for optimal values. Due to lack of tractable algebraic equations, the link between simulations and optimization is oftentimes a surrogate metamodel. However, several forms of uncertainty exist within the cycle that links simulation data, to metamodels, to optimization. Uncertainty may originate from parameters of the simulation, or the form and fitted parameters of the metamodel. This paper reviews different literatures that are relevant to surrogate-based optimization and proposes different strategies for handling uncertainty, by combining machine learning with stochastic programming, robust optimization, and discrepancy modeling. We show that incorporating uncertainty management within simulation-based optimization leads to more robust solutions, which protect the decision-maker from infeasible solutions. Here, we present the results of our proposed approaches through a case study for direct-air capture through temperature swing adsorption.

42 ENGINEERING↗
DOE OSTI2022

Implicit shock tracking for unsteady flows by the method of lines

Here, a recently developed high-order implicit shock tracking (HOIST) framework for resolving discontinuous solutions of inviscid, steady conservation laws is extended to the unsteady case. Central to the framework is an optimization problem which simultaneously computes a discontinuity-aligned mesh and the corresponding high-order approximation to the flow, which provides nonlinear stabilization and a high-order approximation to the solution. This work extends the implicit shock tracking framework to the case of unsteady conservation laws using a method of lines discretization via a diagonally implicit Runge-Kutta method by “solving a steady problem at each timestep”. We formulate and solve an optimization problem that produces a feature-aligned mesh and solution at each Runge-Kutta stage of each timestep, and advance this solution in time by standard Runge-Kutta update formulas. A Rankine-Hugoniot based prediction of the shock location together with a high-order, untangling mesh smoothing procedure provides a high-quality initial guess for the optimization problem at each time, which results in rapid convergence of the sequential quadratic programing (SQP) optimization solver. This method is shown to deliver highly accurate solutions on coarse, high-order discretizations without nonlinear stabilization and recover the design accuracy of the Runge-Kutta scheme. We demonstrate this framework on a series of inviscid, unsteady conservation laws in both one- and two- dimensions. We also verify that our method is able to recover the design order of accuracy of our time integrator in the presence of a strong discontinuity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Sequential Bayesian inference of transition rates in the hidden Markov model for multi-state system degradation

The more easily available system performance data and advances in data analytics have provided us with opportunities to optimize maintenance programs for engineered systems, for example nuclear power plants. One key task in maintenance optimization is to obtain an accurate model for system degradation. Here, in this research, we propose a Bayesian method to address this problem. Noting that systems usually exhibit multiple states and that the actual state of a system usually is not directly observable, in the method we first model the system degradation process and the observation process based on a hidden Markov model. Then we develop a sequential Bayesian inference algorithm based on importance sampling and the forward algorithm to infer the posterior distributions of the transition rates in the hidden Markov model based on available observations. The proposed Bayesian method allows us to take advantage of evidence from multiple sources, and also allows us to perform Bayesian inference sequentially, without the need to use the entire history of observations every time new observations are collected. We demonstrate the proposed method using both synthetic data for a nuclear power plant feedwater pump and realistic data for a nuclear power plant chemistry analytical device.

42 ENGINEERING↗
NASA NTRS1976

An optimal structural design algorithm using optimality criteria

An algorithm for optimal design is given which incorporates several of the desirable features of both mathematical programming and optimality criteria, while avoiding some of the undesirable features. The algorithm proceeds by approaching the optimal solution through the solutions of an associated set of constrained optimal design problems. The solutions of the constrained problems are recognized at each stage through the application of optimality criteria based on energy concepts. Two examples are described in which the optimal member size and layout of a truss is predicted, given the joint locations and loads.

Taylor, J. E.↗
NASA NTRS2024

Simulation Modeling of an Earth Entry System and Solar Radiation Pressure Module Development

Several trajectory simulation tools are used to model different phases of the Approach, Entry, Descent and Landing (AEDL) phase of NASA’s Mars Sample Return Campaign. Working towards a single simulation tool that models all AEDL phases, this project utilizes Program to Optimize Simulated Trajectories II to simulate the 72-hour exoatmospheric phase of AEDL up till the point of Earth atmospheric entry. This involves developing a new feature forPOST2 that captures the forces and moments caused by solar radiation pressure(SRP). Generalizing the vehicle geometry as a combination of primitive shapes, a generalized SRP module was created and implemented into POST2. Trajectory solutions produced by POST2 were then successfully compared against independent solutions produced by the trajectory tool 42.

Program to Optimize Simulated Trajectories II↗
DOE OSTI2021

Optimal decision trees for categorical data via integer programming

Decision trees have been a very popular class of predictive models for decades due to their interpretability and good performance on categorical features. However, they are not always robust and tend to overfit the data. Additionally, if allowed to grow large, they lose interpretability. In this paper, we present a mixed integer programming formulation to construct optimal decision trees of a prespecified size. We take the special structure of categorical features into account and allow combinatorial decisions (based on subsets of values of features) at each node. Our approach can also handle numerical features via thresholding. Here we show that very good accuracy can be achieved with small trees using moderately-sized training sets. The optimization problems we solve are tractable with modern solvers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Applying Graph Partitioning Methods in Measurement-Based Dynamic Load Balancing

Load imbalance can lead to wasted CPU hours especially when running on a large number of processors. Achieving the best parallel efficiency for a program requires optimal load balancing which is a NP-hard problem. Charm++, a migratable objects based programming model, provides a measurement-based dynamic load balancing frame work. This paper explores the use of graph partitioning algorithms, traditionally used for partitioning physical domains/meshes, for measurement based dynamic load balancing of parallel applications. In particular, we present repartitioning methods developed in a graph partitioning toolbox called Scotch that consider the previous mapping to minimize migration costs. We also discuss a new imbalance reduction algorithm for graphs with irregular load distributions. We compare several load balancing algorithms using a micro-benchmark and the NAS BT multi-zone bench mark. New algorithms developed in Scotch lead to better performance compared to other existing partitioners, both in terms of the application execution time and fewer number of objects migrated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A Comparative Assessment of Economic Model Predictive Control Strategies for Fuel Economy Optimization of Heavy-Duty Trucks

This paper provides a comparative assessment of three control strategies that fuse a global, offline dynamic programming (DP) optimization with online model predictive control (MPC) in an effort to minimize fuel consumption for a heavy-duty truck. The online MPC optimization, which is local in nature, makes refinements to a coarsely (but globally, subject to grid resolution) optimized target velocity profile from the DP optimization. Three candidate economic MPC formulations are evaluated: a time-based formulation that directly penalizes predicted fuel consumption, a simplified time-based formulation that penalizes braking effort in place of fuel consumption, and a distance-based convex formulation that maintains a tradeoff between energy expenditure and tracking of the coarsely optimized velocity based on DP. The performance of each approach is presented for three representative route profiles, using a medium-fidelity proprietary Volvo model of the heavy-duty truck’s longitudinal dynamics. Results demonstrate 4-7% fuel economy improvement across all three formulations, when compared to a baseline strategy. Furthermore, we present a detailed analysis of energy usage by “type” (aerodynamic losses, braking losses, and comparison of brake-specific fuel consumption), under each candidate control approach.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Multi-objective optimization of sustainable aviation fuel production pathways in the U.S. Corn Belt

As a potential source of low-carbon transportation energy, biofuels offer certain advantages over vehicle electrification (e.g., lower societal vulnerability to grid failures, and improved range of sustainable aviation), but also several challenges, including cost, carbon intensity, and land usage. There are also well-founded concerns that biofuel supply chains could be disrupted if extreme weather events impact feedstock yields. In this paper, we explore the use of multi-objective optimization to identify biofuel production pathways that balance cost, greenhouse gas emissions, and supply vulnerability to extreme weather. We compare the use of three different many-objective evolutionary algorithms and linear programming in optimizing biomass cultivation decisions in the U.S. Corn Belt under weather uncertainty using historical, modeled, and synthetic yield data. We consider four feedstock choices (corn, soy, switchgrass, and algae) with two land types (agricultural and marginal lands) and evaluate decisions using three alternative spatial resolutions (ranging from the USDA agricultural district level to the state level). Results show that feedstock choice is the primary driver of objective performance (i.e., the position and shape of 3D, approximate Pareto frontiers). Spatial diversification is a less effective tool in reducing exposure to weather-caused drops in crop yield.

09 BIOMASS FUELS↗
DOE OSTI2020

DER Planning with Resilience Analysis Using REopt Lite: A Behind-the-Meter Techno-Economic Analysis Tool

REopt Lite is a techno-economic decision support model for behind-the-meter energy systems design and dispatch modeling. REopt Lite is used to optimize energy systems for buildings, campuses, communities, and microgrids. It is based on a Mixed Integer Linear Programming (MILP) optimization model. It is a fully automated and streamlined energy modeling tool that can be used off-the-shelf for a wide variety of distributed generation integration analyses and at the same time is architected to be extensible for user-specific customizations for advanced users and subject matter experts. It is publicly available as a webtool as well as has an Application Programming Interface (API). The API functionality enables programmatic access to the model facilitating smooth integration with other distribution systems modeling tools, and automated multiple scenarios/sensitivity studies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1991

Optimal aircraft performance during microburst encounter

The effects of microburst characteristics on the optimal penetration performance of jet transport and general aviation aircraft are presented. The purpose is to determine the best possible performance that can be achieved in a broad range of microbursts. A secondary goal is to illustrate good strategies for dealing with a range of microbursts during takeoff and landing. Over 1100 optimal trajectories were computed for two aircraft types flying through idealized microbursts using a Successive Quadratic Programs trajectory optimization algorithm. Contours of safety metrics are plotted as functions of the length scales, magnitudes, and locations of horizontal wind shears and vertical downdrafts. These performance contours show three length-scale regimes for optimal microburst penetration. At short length scales, hazards usually associated with gustiness predominate (e.g., high normal load factor, rotational upset). At intermediate length scales, a degraded ability to maintain flight path and/or vertical velocity poses the most serious threat. At very long microburst length scales, excessive touchdown velocities may result. The ability to transit a microburst successfully also varies strongly with microburst location. The results show that both aircraft types could penetrate some very severe microbursts if optimal control histories were followed. Nevertheless, these control strategies assume perfect prior knowledge of the wind, and practical limits to successful encounter with real-time control capabilities would be lower. The optimally controlled jet transport can successfully penetrate higher intensity microbursts than can the general aviation aircraft.

Psiaki, Mark L.↗
NASA NTRS1994

Structural optimization with approximate sensitivities

Computational efficiency in structural optimization can be enhanced if the intensive computations associated with the calculation of the sensitivities, that is, gradients of the behavior constraints, are reduced. Approximation to gradients of the behavior constraints that can be generated with small amount of numerical calculations is proposed. Structural optimization with these approximate sensitivities produced correct optimum solution. Approximate gradients performed well for different nonlinear programming methods, such as the sequence of unconstrained minimization technique, method of feasible directions, sequence of quadratic programming, and sequence of linear programming. Structural optimization with approximate gradients can reduce by one third the CPU time that would otherwise be required to solve the problem with explicit closed-form gradients. The proposed gradient approximation shows potential to reduce intensive computation that has been associated with traditional structural optimization.

Patnaik, S. N.↗