SEARCH · Engineering Papers

Results for “Cost 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.

SourceResearch connectionFrom yearThrough year

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

At least 865 records · Page 48

All sources NASA NTRS DOE OSTI

NASA NTRS2018

A Common Probe Design for Multiple Planetary Destinations

Atmospheric probes have been successfully flown to planets and moons in the solar system to conduct in situ measurements. They include the Pioneer Venus multi-probes, the Galileo Jupiter probe, and Huygens probe. Probe mission concepts to five destinations, including Venus, Jupiter, Saturn, Uranus, and Neptune, have all utilized similar-shaped aeroshells and concept of operations, namely a 45-degree sphere cone shape with high density heatshield material and parachute system for extracting the descent vehicle from the aeroshell. Each concept designed its probe to meet specific mission requirements and to optimize mass, volume, and cost. At the 2017 International Planetary Probe Workshop (IPPW), NASA Headquarters postulated that a common aeroshell design could be used successfully for multiple destinations and missions. This "common probe" design could even be assembled with multiple copies, properly stored, and made available for future NASA missions, potentially realizing savings in cost and schedule and reducing the risk of losing technologies and skills difficult to sustain over decades. Thus the NASA Planetary Science Division funded a study to investigate whether a common probe design could meet most, if not all, mission needs to the five planetary destinations with extreme entry environments. The Common Probe study involved four NASA Centers and addressed these issues, including constraints and inefficiencies that occur in specifying a common design. Study methodology: First, a notional payload of instruments for each destination was defined based on priority measurements from the Planetary Science Decadal Survey. Steep and shallow entry flight path angles (EFPA) were defined for each planet based on qualification and operational g-load limits for current, state-of-the-art instruments. Interplanetary trajectories were then identified for a bounding range of EFPA. Next, 3-degrees-of-freedom simulations for entry trajectories were run using the entry state vectors from the interplanetary trajectories. Aeroheating correlations were used to generate stagnation point convective and radiative heat flux profiles for several aeroshell shapes and entry masses. High fidelity thermal response models for various Thermal Protection System (TPS) materials were used to size stagnation-point thicknesses, with margins based on previous studies. Backshell TPS masses were assumed based on scaled heat fluxes from the heatshield and also from previous mission concepts. Presentation: We will present an overview of the study scope, highlights of the trade studies and design driver analyses, and the final recommendations of a common probe design and assembly. We will also indicate limitations that the common probe design may have for the different destinations. Finally, recommended qualification approaches for missions will be presented.

Hwang, H. H.↗

NASA NTRS2018

A Common Probe Design for Multiple Planetary Destinations

Atmospheric probes have been successfully flown to planets and moons in the solar system to conduct in-situ measurements. They include the Pioneer Venus multi-probes, the Galileo Jupiter probe, and Huygens probe. Probe mission concepts to five destinations, including Venus, Jupiter, Saturn, Uranus, and Neptune, have all utilized similar-shaped aeroshells and concept of operations, namely a 45 deg sphere cone shape with high density heatshield material and parachute system for extracting the descent vehicle from the aeroshell. Each concept designed its probe to meet specific mission requirements and to optimize mass, volume, and cost. At the 2017 IPPW, NASA Headquarters postulated that a common aero-shell design could be used successfully for multiple destinations and missions. This "common probe" design could even be assembled with multiple copies, properly stored, and made available for future NASA missions, potentially realizing savings in cost and schedule and reducing the risk of losing technologies and skills difficult to sustain over decades. Thus the NASA Planetary Science Division funded a study to investigate whether a common probe design could meet most, if not all, mission needs to the five planetary destinations with extreme entry environments. The Common Probe study involved four NASA Centers and addressed these issues, including constraints and inefficiencies that occur in specifying a common design.Study methodology: First, a notional payload of instruments for each destination was defined based on priority measurements from the Planetary Science Decadal Survey. Steep and shallow entry flight path angles (EFPA) were defined for each planet based on qualification and operational g-load limits for current, state-of-the-art instruments. Interplanetary trajectories were then identified for a bounding range of EFPA. Next, 3-DoF simulations for entry trajectories were run using the entry state vectors from the interplanetary trajectories. Aeroheating correlations were used to generate stagnation point convective and radiative heat flux profiles for several aeroshell shapes and entry masses. High fidelity thermal response models for various TPS materials were used to size stagnation point thicknesses, with margins based on previous studies. Backshell TPS masses were assumed based on scaled heat fluxes from the heatshield and also from previous mission concepts.

Hwang, Helen H.↗

NASA NTRS2018

A Common Probe Design for Multiple Planetary Destinations

Hwang, Helen H.↗

NASA NTRS2018

A Common Probe Design for Multiple Planetary Destinations

Introduction: Atmospheric probes have been successfully flown to planets and moons in the solar system to conduct in situ measurements. They include the Pioneer Venus multiprobes, the Galileo Jupiter probe, and Huygens probe. Probe mission concepts to five destinations, including Venus, Jupiter, Saturn, Uranus, and Neptune, have all utilized similarshaped aeroshells and concept of operations, namely a 45° sphere cone shape with high density heatshield material and parachute system for extracting the descent vehicle from the aeroshell. The current paradigm is to design a probe to meet specific mission requirements and to optimize mass, volume, and cost for a single mission. However, this methodology means repeated efforts to design an aeroshell for different destinations with minor differences. A new paradigm has been explored that has a "common probe" design that could be flown at these different destinations and could be assembled in advance with multiple copies, properly stored, and made available for future NASA missions.

Hwang, Helen H.↗

NASA NTRS2022

Realizability Checking of Requirements in FRET

Requirements formalization has become increasingly popular in industrial settings as an effort to disambiguate designs and optimize development time and costs for critical system components. Formal requirements elicitation also enables the employment of analysis tools to prove important properties, such as consistency and realizability. In this report, we present the realizability analysis framework that we developed as part of the Formal Requirements Elicitation Tool (FRET). Our framework prioritizes usability, and employs state-of-the-art analysis algorithms that support infinite theories. We demonstrate the workflow for realizability checking, showcase the diagnosis process that supports visualization of conflicts between requirements and simulation of counterexamples, and discuss results from industrial-level case studies.

Formal Requirements Elicitation Tool↗

NASA NTRSDate not supplied

Turbine Electrified Energy Management with Model Predictive Control

Affordability, sustainability, and efficiency are primary motivators driving the future of NASA aeronautics research. These factors are realized, in part, through the development and implementation of new technologies and strategies enabling efficient, affordable, and safe hybrid-electric aircraft. Research supporting electrified aircraft propulsion control systems exemplifies such new methodologies, offering varied opportunities to integrate electric machines with gas-based turbine engines. For hybrid-electric propulsion systems, current conceptual architectures seek to introduce energy storage and exploit electrical power system components to assist gas-based system components. Capitalizing on the electric machines in hybridized engines, Turbine Electrified Energy Management (TEEM) is a control approach that enhances transient operability to improve overall propulsion and vehicle efficiency by injecting or extracting power from engine shafts. Traditionally implemented with proportional-integral (PI) control, this study expands the application of TEEM by presenting model predictive control (MPC) schemes to execute the TEEM concept. Via cost function design and constraint selection, the transient operability goals for TEEM are considered in the controller designs. The proposed MPCs are simulated on a nonlinear turbofan engine model at two environmental conditions, with comparisons drawn to a baseline PI. Performance is evaluated using compressor maps and two TEEM-specific metrics: transient stack usage and transient excursion integral. Simulation results reveal the developed schemes perform comparably to the benchmark controller and can be implemented in two distinct configurations. Potential modifications for future investigations include cost function measures that optimize energy use, additional performance effectiveness measures, and battery storage capabilities.

Elyse D. Hill↗

DOE OSTI2021

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL's Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Safeguards Modeling for Advanced Nuclear Facility Design.

Future nuclear fuel cycle facilities will see a significant benefit from considering materials accountancy requirements early in the design process. The Material Protection, Accounting, and Control Technologies (MPACT) working group is demonstrating Safeguards and Security by Design (SSBD) for a notional electrochemical reprocessing facility as part of a 2020 Milestone. The idea behind SSBD is to consider regulatory requirements early in the design process to provide more optimized systems and avoid costly retrofits later in the design process. Safeguards modeling, using single analyst tools, allows the designer to efficiently consider materials accountancy approaches that meet regulatory requirements. However, safeguards modeling also allows the facility designer to go beyond current regulations and work toward accountancy designs with rapid response and lower thresholds for detection of anomalies. This type of modeling enables new safeguards approaches and may inform future regulatory changes. The Separation and Safeguards Performance Model (SSPM) has been used for materials accountancy system design and analysis. This paper steps through the process of designing a Material Control and Accountancy (MC&A) system, presents the baseline system design for an electrochemical reprocessing facility, and provides performance metrics from the modeling analysis. The most critical measurements in the electrochemical facility are the spent fuel input, electrorefiner salt, and U/TRU product output measurements. Finally, material loss scenario analysis found that measurement uncertainties (relative standard deviations) for Pu would need to be at 1% (random and systematic error components) or better in order to meet domestic detection goals or as high as 3% in order to meet international detection goals, based on a 100 metric ton per year plant size.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2022

Low Cost Cloud Based Resource Characterization Tools For Performance Monitoring and Optimization

Explore the source record for details and available documents.

13 HYDRO ENERGY↗

DOE OSTI2024

Multi-functional Sorbent Technology (MUST) for the Recovery/Removal of Critical/Heavy Metals from Fossil-Related Wastewater

A collection of remediation and recovery technologies like those predicated on photocatalytic-, electric-, chemical-, membrane-, and adsorptive-based processes currently exists in different stages of development or deployment between these methods of water management. Among these technologies, solid sorbent processes embody an optimal balance between cost-effectiveness, environmental friendliness, and technological maturity. The U.S. Department of Energy’s National Energy Technology Laboratory developed a suit of cross-linked, functionalized silica sorbents tailored to eliminate the most toxic metals regulated by the U.S. Environmental Protection Agency (EPA); critical metals identified by the U.S. Geological Survey (USGS); and harmful organics like the infamous perfluoroalkyl and polyfluoroalkyl substances (PFAS). An array of laboratory and field tests proved that the MUST sorbents removed ppb-level lead from drinking water, selenium from FGD wastewater below EPA limits, aqueous dyes and PFAS; and fractionated low ppm-level critical metals (CM) from AMD and simulated produced water. Proof-of-concept for commercial CM recovery was verified through obtaining milligram-quantities of purified Al solids from a sorbent multi-bed AMD field-site test. Commercial efficacy was further supported by achieving purified fractions of adsorbed Mn upon treating both the AMD and synthetic produced water with the laboratory multi-bed test unit.

Wilfong, Walter C.↗

DOE OSTI2021

U.S. Domestic Pebble Bed Reactor: Security-by-Design

U.S. nuclear power facilities face increasing challenges in meeting dynamic security requirements caused by evolving and expanding threats while keeping cost reasonable to make nuclear energy competitive. The past approach has often included implementing security features after a facility has been designed and without attention to optimization, which can lead to cost overruns. Incorporating security in the design process can provide robust, cost effective, and sufficient physical protection systems. The purpose of this work is both to develop a framework for the integration of security into the design phase of High Temperature Gas Reactors (HTGRs) that utilize pebble-based fuels and increase the use of modeling and simulation tools to optimize the design of physical protection systems. Specifically, this effort focuses on integrating security into the design phase of a model HTGR that meets current Nuclear Regulatory Commission (NRC) physical protection requirements and providing advanced solutions to improve physical protection and decrease costs. A suite of tools, including SCRIBE3D©, PATHTRACE© and Blender© were used to model a hypothetical, generic domestic HTGR facility. Physical protection elements such as sensors, cameras, barriers, and guard forces were added to the model based on best practices for physical protection systems. Multiple outsider sabotage scenarios were examined with four-to eight adversaries to determine security metrics. The results of this work will influence physical protection system designs and facility designs for U.S. domestic HTGRs. This work will also demonstrate how a series of experimental and modeling capabilities across the Department of Energy (DOE) Complex can impact the design of and complete Safeguards and Security by Design (SSBD) for SMRs. The conclusions and recommendations in this document may be applicable to all SMR designs.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2021

Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization

In recent years, the use of machine learning-based surrogate models for computational fluid dynamics (CFD) simulations has emerged as a promising technique for reducing the computational cost associated with engine design optimization. However, such methods still suffer from drawbacks. One main disadvantage is that the default machine learning (ML) hyperparameters are often severely suboptimal for a given problem. This has often been addressed by manually trying out different hyperparameter settings, but this solution is ineffective in case of a high-dimensional hyperparameter space. Besides this problem, the amount of data needed for training is also not known a priori. In response to these issues that need to be addressed, the present work describes and validates an automated active learning approach, AutoML-GA, for surrogate-based optimization of internal combustion engines. In this approach, a Bayesian optimization technique is used to find the best machine learning hyperparameters based on an initial dataset obtained from a small number of CFD simulations. Subsequently, a genetic algorithm is employed to locate the design optimum on the ML surrogate surface. In the vicinity of the design optimum, the solution is refined by repeatedly running CFD simulations at the projected optima and adding the newly obtained data to the training dataset. It is demonstrated that AutoML-GA leads to a better optimum with a lower number of CFD simulations, compared to the use of default hyperparameters. The proposed framework offers the advantage of being a more hands-off approach that can be readily utilized by researchers and engineers in industry who do not have extensive machine learning expertise.

Owoyele, Opeoluwa↗

DOE OSTI2025

Hybrid data-driven cement-stabilized soil design: An integration of machine learning, multi-objective optimization, and life cycle assessment

Soil stabilization is crucial in geotechnical engineering, yet conventional methods are often time-consuming, resource-intensive, and environmentally unsustainable. Despite growing interest in Machine Learning (ML) and optimization tools for mix design, few studies integrate these methods with decision-making techniques and environmental assessment to support practical implementation. This study proposes a hybrid data-driven framework for predicting strength, optimizing mix compositions, and evaluating environmental impacts via life cycle assessment of cement-stabilized soft soils. Six ML models were evaluated, and the top-performing eXtreme Gradient Boosting (XGB) model was further improved using the Grey Wolf Optimizer (GWO). The optimized XGB-GWO model, integrated with a polynomial cost function, served as the objective function in a multi-objective optimization problem solved via the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), with final mix selection guided by the entropy-weighted TOPSIS method. Validation through a case study produced mix designs offering superior strength-cost trade-offs, with the optimal mix achieving 2243.2 kPa unconfined compressive strength and a 16.07 % reduction in carbon emissions compared to the highest-cost design. In conclusion, this study offers a sustainable, scalable approach to soil stabilization and supports informed decision-making in construction.

Life cycle assessment↗

NASA NTRS2019

Exploring Network-Related Optimization Problems Using Quantum Heuristics

Network-related connectivity optimization problems are underlying a wide range of applications and are also of high computational complexity. We consider studying network optimization problems using two types of quantum heuristics.One is quantum annealing, and the other Quantum Alternating Operator Ansatz, an extension of the Quantum Approximate Optimization Algorithms for gate-model quantum computation, in which a cost-function based unitary and a non-commuting mixing unitary are applied alternately. We present problem mappings for problems of finding the spanning-tree or spanning-graph of a graph that optimizes certain costs, and a variant that further requires the spanning-tree be degree-bounded. With quantum annealing, all constraints are cast into penalty terms in the cost Hamiltonian, and the solution is encoded as the ground state of the Hamiltonian. We provide three mappings to the quadratic unconstrained binary optimization (QUBO) form, compare the resource requirements, and analyze the tradeoffs. For QAOA, we give special focus on the design of mixers based on the constraints presented in the problem, such that the system evolution remains in a subspace of the full Hilbert space where all constraints are satisfied. In the spanning-tree problem, one such hard constraint is that a mixer applied to a spanning-tree needs also be a spanning tree. This involves checking the connectivity of a subgraph, which is a global condition common for most network-related problems. We show how this feature can be efficiently represented in the mixer in a quantum coherent way, based on manipulation of a descendant-matrix and an adjacent matrix. We further develop a mixer for the spanning-graphs based on the spanning-tree mixer.

Wang, Zhihui↗

NASA NTRS2019

Study network-related optimization problems using quantum alternating optimization ansatz

Network-related connectivity optimization problems are underlying a wide range of applications and are also of high computational complexity. We consider studying network optimization problems using two types of quantum heuristics. One is quantum annealing, and the other Quantum Alternating Operator Ansatz, an extension of the Quantum Approximate Optimization Algorithms for gate-model quantum computation, in which a cost-function based unitary and a non-commuting mixing unitary are applied alternately. We present problem mappings for problems of finding the spanning-tree or spanning-graph of a graph that optimizes certain costs, and a variant that further requires the spanning-tree be degree-bounded. With quantum annealing, all constraints are cast into penalty terms in the cost Hamiltonian, and the solution is encoded as the ground state of the Hamiltonian. We provide three mappings to the quadratic unconstrained binary optimization (QUBO) form, compare the resource requirements, and analyze the tradeoffs. For QAOA, we give special focus on the design of mixers based on the constraints presented in the problem, such that the system evolution remains in a subspace of the full Hilbert space where all constraints are satisfied. In the spanning-tree problem, one such hard constraint is that a mixer applied to a spanning-tree needs also be a spanning tree. This involves checking the connectivity of a subgraph, which is a global condition common for most network-related problems. We show how this feature can be efficiently represented in the mixer in a quantum coherent way, based on manipulation of a descendant-matrix and an adjacent matrix. We further develop a mixer for the spanning-graphs based on the spanning-tree mixer.

Zhihui Wang↗

DOE OSTI2020

Smart Composite Pressure Vessels (SCPV) with Integrated Health Monitoring

The U.S. Department of Energy (DOE) is promoting and developing more energy efficient and environmentally friendly technologies that will enable America to use less petroleum. Hydrogen fuel cells – which directly convert the chemical energy in hydrogen to electricity with only water and heat as byproducts – are a very attractive solution that can enable this to happen. To date, DOE's efforts have culminated in commercial demonstration of on-board vehicular hydrogen storage systems that can allow for a driving range of greater than 300 miles. This requires storing 5 Kgs of hydrogen onboard a light vehicle. In order to store this quantity of hydrogen, the hydrogen gas needs to be stored in a composite overwrapped pressure vessel (COPV) at a very high internal pressure. The objective of the program was to demonstrate continuous and predictable health-monitoring of composite pressure vessels. A higher confidence in the operational safety of the vessels will lead to reduced burst factor of safety imposed by regulatory standards and hence will help reduce the structural wall thickness that drives the cost of the vessel. Highly optimized Type III (metal lined) and Type IV (polymer lined) COPVs that are manufactured using filament winding process have been designed and qualified for the above mentioned application. However, the main structural component of the COPVs is carbon fiber and the high cost of carbon fiber composite in a pressure vessel is a primary challenge in reducing the cost of gaseous hydrogen storage. Continuous and remote monitoring of structural health of the COPVs as well as optimization of the strength translation of carbon fibers have the potential to allow for reduced factor of safety, thereby reduced amount of carbon and associated cost of the vessel. In the current program, the project team including Steelhead Composites (SHC), University of Tennessee, Knoxville (UTK), Teijin Carbon America, Oak Ridge National Laboratory (ORNL) and LUNA designed, fabricated, and tested smart composite pressure vessels with integrated sensors. Iterative loops of design and testing using coupons, subscale STEB vessels and full size vessels proved that a new generation of high performance carbon fiber can be used for efficient design of Type 3 vessels for H2 storage. Novel analysis techniques were developed to predict the initiation and propagation of interlaminar damage inside the composite shell due to impact damage, a real threat in practical operation. Such an analysis scheme is typically not used in tank design but is an essential tool for health monitoring of composite vessels. Remote sensing of the key signatures of the tank’s operating parameters such as pressure, acceleration and humidity was demonstrated using a unique device that can wirelessly transmit and stream the data to a remote server. Fiber optic sensors were successfully integrated during fabrication of the tanks. These sensors provide a wealth of information regarding the structural health of the vessel when it is subsequently pressurized or subjected to impact damage. Excellent correlation was demonstrated between the fiber optic sensor and mechanical strain gage data, and between the measurements and analytical predictions.

08 HYDROGEN↗

NASA NTRS1983

Large-scale structural optimization

Problems encountered by aerospace designers in attempting to optimize whole aircraft are discussed, along with possible solutions. Large scale optimization, as opposed to component-by-component optimization, is hindered by computational costs, software inflexibility, concentration on a single, rather than trade-off, design methodology and the incompatibility of large-scale optimization with single program, single computer methods. The software problem can be approached by placing the full analysis outside of the optimization loop. Full analysis is then performed only periodically. Problem-dependent software can be removed from the generic code using a systems programming technique, and then embody the definitions of design variables, objective function and design constraints. Trade-off algorithms can be used at the design points to obtain quantitative answers. Finally, decomposing the large-scale problem into independent subproblems allows systematic optimization of the problems by an organization of people and machines.

Sobieszczanski-Sobieski, J.↗

DOE OSTI2024

Optimization of Solid Oxide Electrolysis Cell Systems Accounting for Long-Term Performance and Health Degradation

This study focuses on optimizing solid oxide electrolysis cell (SOEC) systems for efficient and durable long-term hydrogen (H2) production. While the elevated operating temperatures of SOECs offer advantages in terms of efficiency, they also lead to chemical degradation, which shortens cell lifespan. To address this challenge, dynamic degradation models are coupled with a steady-state, two-dimensional, non-isothermal SOEC model and steady-state auxiliary balance of plant equipment models, within the IDAES modeling and optimization framework. A quasi-steady state approach is presented to reduce model size and computational complexity. Long-term dynamic simulations at constant H2 production rate illustrate the thermal effects of chemical degradation. Dynamic optimization is used to minimize the lifetime cost of H2 production, accounting for SOEC replacement, operating, and energy expenses. Several optimized operating profiles are compared by calculating the Levelized Cost of Hydrogen (LCOH).

Giridhar, Nishant↗