Search NASA⌕ Search

SEARCH · Search NASA

Results for “multi objective”

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.

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

At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The MOST Hosts Survey: Spectroscopic Observation of the Host Galaxies of ∼40,000 Transients Using DESI

We present the Multi-Object Spectroscopy of Transient (MOST) Hosts survey. The survey is planned to run throughout the 5 yr of operation of the Dark Energy Spectroscopic Instrument (DESI) and will generate a spectroscopic catalog of the hosts of most transients observed to date, in particular all the supernovae observed by most public, untargeted, wide-field, optical surveys (Palomar Transient Factory, PTF/intermediate PTF, Sloan Digital Sky Survey II, Zwicky Transient Facility, DECAT, DESIRT). Science cases for the MOST Hosts survey include Type Ia supernova cosmology, fundamental plane and peculiar velocity measurements, and the understanding of the correlations between transients and their host-galaxy properties. Here we present the first release of the MOST Hosts survey: 21,931 hosts of 20,235 transients. These numbers represent 36% of the final MOST Hosts sample, consisting of 60,212 potential host galaxies of 38,603 transients (a transient can be assigned multiple potential hosts). Of all the transients in the MOST Hosts list, only 26.7% have existing classifications, and so the survey will provide redshifts (and luminosities) for nearly 30,000 transients. A preliminary Hubble diagram and a transient luminosity–duration diagram are shown as examples of future potential uses of the MOST Hosts survey. The survey will also provide a training sample of spectroscopically observed transients for classifiers relying only on photometry, as we enter an era when most newly observed transients will lack spectroscopic classification. The MOST Hosts DESI survey data will be released on a rolling cadence and updated to match the DESI releases.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Diverse Chemo-Dynamical Properties of Nitrogen-Rich Stars Identified from Low-Resolution Spectra

The second generation of stars in the globular clusters (GCs) of the Milky Way (MW) exhibit unusually high N, Na, or Al, compared to typical Galactic halo stars at similar metallicities. The halo field stars enhanced with such elements are believed to have originated in disrupted GCs or escaped from existing GCs. We identify such stars in the metallicity range –3.0 < [Fe/H] < 0.0 from a sample of ~ 36,800 giant stars observed in the Sloan Digital Sky Survey and Large Sky Area Multi-Object Fiber Spectroscopic Telescope survey, and present their dynamical properties. The N-rich population (NRP) and N-normal population (NNP) among our giant sample do not exhibit similarities in either in their metallicity distribution function (MDF) or dynamical properties. We find that, even though the MDF of the NRP looks similar to that of the MW’s GCs in the range of [Fe/H] < –1.0, our analysis of the dynamical properties does not indicate similarities between them in the same metallicity range, implying that the escaped members from existing GCs may account for a small fraction of our N-rich stars, or the orbits of the present GCs have been altered by the dynamical friction of the MW. We also find a significant increase in the fraction of N-rich stars in the halo field in the very metal-poor (VMP; [Fe/H] < –2.0) regime, comprising up to ~ 20% of the fraction of the N-rich stars below [Fe/H] = –2.5, hinting that partially or fully destroyed VMP GCs may have in some degree contributed to the Galactic halo. A more detailed dynamical analysis of the NRP reveals that our sample of N-rich stars do not share a single common origin. Although a substantial fraction of the N-rich stars seem to originate from the GCs formed in situ, more than 60% of them are not associated with those of typical Galactic populations, but probably have extragalactic origins associated with Gaia Sausage/Enceladus, Sequoia, and Sagittarius dwarf galaxies, as well as with presently unrecognized progenitors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Optimal Design of Food Packaging Considering Waste Management Technologies to Achieve Circular Economy

Plastic packaging plays a fundamental role in the food industry, avoiding food waste and facilitating food access. The increasing plastic production and the lack of appropriate plastic waste management technologies represent a threat to the environmental and human welfare. Therefore, there is an urgent need to identify sustainable packaging solutions. Circular economy (CE) promotes reducing waste and increasing recycling practices to achieve sustainability. In this work, we propose a CE framework based on multi-objective optimization, considering both economic and environmental impacts, to identify optimal packaging designs and waste management technologies. Using mixed-integer linear programming (MILP), techno-economic analysis (TEA), and life cycle assessment (LCA), this work aims to build the first steps in packaging design, informing about the best packaging alternatives and the optimal technology or technologies to process packaging waste. For the economic analysis, we consider the minimum increase in price (MIP) when adding recycling to the cost of each packaging solution, while for the environmental analysis, the greenhouse gas emissions impact was considered. A case study on ground coffee packaging is used to illustrate the proposed framework. The results demonstrate that the multilayer bag option is the most convenient when considering both the chosen economic and environmental impacts.

Life Cycle Analysis↗
DOE OSTI2020

Conjugate Heat Transfer Analysis of the Supercritical CO2 Based Counter Flow Compact 3D Heat Exchangers

Compact heat exchangers using supercritical fluids such as CO2 are preferred due to their high heat transfer capacity and smaller footprint. Three-dimensional conjugate forced convection heat transfer analysis was performed on several shell-and-tube counter-flow microchannel heat exchangers. Numerical simulations were conducted to test effect of change in mass flow rate, hydraulic diameter and various cross sections on the heat transfer. Increasing mass flow rate improved heat transfer up to a maximum value and then decreased downstream with increasing turbulence. Maximum heat transfer was obtained for the micro channel with the smallest hydraulic diameter. Amongst the cross sections analyzed (circular, square, circular with radial ribs, and square with radial ribs), the most uniform distribution of temperature and maximum heat transfer were obtained for circular cross section with radial ribs. An optimally efficient operation of such a heat exchanger can be attained by considering these factors during multi-objective constrained optimization of geometric parameters and requirements for additive manufacturing of such compact heat exchangers.

Chitale, Janhavi↗
DOE OSTI2021

Optimization of Refrigerant Compositions for Low-GWP Refrigerant Mixtures Using Segment-by-segment Heat Exchanger and Detailed System Models

The recently introduced hydrofluoroolefin (HFO) refrigerants, including R1234yf and R1234ze(E), have significantly lower global warming potentials (GWPs) than traditional hydrofluorocarbon (HFC) refrigerants like R410A. However, prior tests show that direct drop-in of pure R1234yf or R1234ze(E) into equipment designed for R410A results in a decrease in heat exchanger capacity and the system coefficient of performance. The primary reason is the lower in-tube heat transfer performance of R1234yf and R1234ze(E) compared with that of R410A. To address this issue, previous studies have mixed the mildly flammable HFC R32 with HFOs to improve system performance, with HFC R125 also added to suppress flammability. Previous studies selected compositions based on simple cycle analyses and did not consider modifications of the heat exchanger circuitry configuration to adapt to the new refrigerants. This study presents a novel multi-objective optimization approach to design a refrigerant composition that maximizes energy efficiency within flammability and GWP limits. The approach in this work simultaneously optimizes mixture composition and heat exchanger circuitry configuration. A case study on a rooftop unit indicates that, compared with mixture-only optimization, simultaneous optimization of mixture and heat exchanger circuitry yields a 5.9% improvement in cycle efficiency and a 48.6% reduction in refrigerant flammability with a GWP of 268. Circuitry optimization using refrigerants with different temperature glides shows that the larger the temperature glide is, the larger EER improvement is obtained. The results show that zeotropic blends with a large temperature glide are more sensitive to the refrigerant circuitry than pure refrigerants and may suffer significant performance degradation with subpar heat exchanger circuitry design. The proposed optimization approach is generally applicable to mixtures with any number of components. Using this approach to design a HVAC system can yield higher system efficiency within flammability and GWP constraints.

Li, Zhenning↗
DOE OSTI2021

Domain knowledge-informed, process-mapping AI graph for designing Fe-based alloys

<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Continuous improvement in efficiency of a power plant relies on designing materials for use at increasingly higher temperature and/or pressure, for 100,000s hours of operation. Due to complexity, non-linearity and high-dimensionality of the problem, traditional Machine Learning (ML) approaches require unreasonably large datasets for the data-driven model development. Science-based material and process engineering complements hard data with, sometimes soft and intuitive, empirical domain knowledge. Artificial Intelligence (AI) was used in this study to incorporate such knowledge into computational graph architecture (process-mimicking artificial neuron design, causal layer and graph structures, ensemble modeling of latent states) and learning procedures (variable transformation, fuzzy physics pre-training and freezing of deep layers, virtual microstructure representation, and adversarial multi-objective optimization). The first alloys design pathways suggested by the AI tool (pyroMind) passed a preliminary engineering review on soundness and transparency.</span>

Romanov, Vyacheslav↗
DOE OSTI2022

Critical Analysis of Replacements for R-410A in Heat Pump Applications

Using low-GWP refrigerants can reduce the Green House Gas (GHG) emission of HVAC systems. Research has shown that using heat exchangers with small diameter tubes is a promising solution to meet the performance goals of heat pump using low-GWP refrigerants due to reduced refrigerant charge, reduced flammable impact and environmental impact. However, application of small diameter tube requires in-depth component design optimization to make the new system adapt to low-GWP refrigerants.In this paper, multi-objective optimizations using Particle Swarm Optimization (PSO) algorithm on a R-410A residential 5-ton air source heat pump is performed for improved system performance and reduced material cost. Five R-410A alternatives, i.e., R-32, R-454A, R-454B, R-454C and R-455A are investigated. R-455A and R-454C have GWP lower than 150. As a result of optimization, 12.4%-19.1% Energy Efficiency Ratio (EER) improvement and up to 71% HXs material cost saving is achieved. Life Cycle Climate Performance (LCCP) analysis shows that optimized systems reduce total CO2 emission by 13%-33% depending on the choice of refrigerant and climate zone.The optimal heat exchangers resulting from this research can fit into the original R-410A fan-coil units. The proposed heat pump design method establishes a production and installation path to produce cost-effective low-GWP heat pumps easily accepted by end users.

Li, Zhenning↗
DOE OSTI2023

Optimization of a Residential Air Source Heat Pump using Refrigerants with GWP <150 for Improved Performance and Reduced Emission

Using low-GWP refrigerants can reduce the Green House Gas (GHG) emission of heat pump systems. Heat exchangers and compressors are the key components and have a prominent impact on system performance, significant research is devoted to reducing the cost of the heat exchangers while achieving the same or better system performance with refrigerant charge reduction.To better understand the environmental impacts of optimized systems with low-GWP refrigerants, Life Cycle Climate Performance (LCCP) evaluation method was used to evaluate the direct and indirect emissions of the system over the course of its lifetime from manufacturing to disposal. The DOE/ORNL Heat Pump Design Model (HPDM) is used to evaluate the performance of heat pumps. Multi-objective optimizations using Particle Swarm Optimization (PSO) algorithm are performed on a 3-ton R410A residential air source heat pump on market. Seven R410A alternatives, i.e., R32, R454B, R454C, R455A, R457A, R1234yf and R1234ze(E) are investigated. The last five fluids have GWP lower than 150.As a result, 5.5%-12.8% seasonal energy efficiency ratio 2 (SEER2) improvement is achieved, and the optimized systems reduce life cycle CO2 emission by 8.5%-28.6% with GWP lower than 150 refrigerants. The optimal heat exchangers can fit into the original R410A fan-coil units; therefore, the proposed design method establishes a production and installation path to produce cost-effective low-GWP heat pumps easily accepted by end users.

Li, Zhenning↗
DOE OSTI2023

Modeling the CEBAF Injector at 200 kV: Investigating K-Long Beam Conditions with and without Wien Filter

The upcoming K-Long experiment [1] ain Hall D at Jefferson Lab presents unique beam requirements, featuring a significantly low bunch repetition rate and an unusually high bunch charge. This experiment, which utilizes the CEBAF accelerator in conjunction with the GlueX experimental setup, aims to study strange hadron spectroscopy by measuring the differential cross section and polarizations of produced hyperons such as Λ, Σ, Ξ, and Ω. By directing an intense K-Long beam towards the LD2/LH2 target, new and valuable data can be obtained. To optimize the CEBAF injector specifically for this experiment, we employed Multi-Objective Genetic Optimization (MGO) using General Particle Tracer (GPT) simulations. Through this approach, we determined the optimal magnetic elements and radiofrequency (RF) settings required to achieve a K-Long bunch charge of 0.64 pC at an energy of 200 kV. We conducted simulations with both the Wien Filter turned on and off to examine its impact on the beam. Furthermore, we investigated the transmission efficiency and beam characteristics of electron beams with varying charge per bunch through the injector, considering the simultaneous operation of all four CEBAF Halls. The results of our study offer valuable insights and guidance for optimizing the CEBAF injector not only for the Jefferson Lab K-Long experiment but also for other experiments that entail similar beam conditions.

Pokharel, Sunil↗
DOE OSTI2022

Consideration of alternative coolants in AGR-like FHR designs

Replacing carbon-dioxide coolant in AGR-like reactor with molten salt significantly affects the reactor performance characteristics. Hence, a new core design is needed. In this work, a Multi-Objective Particle Swarm Optimisation is used to identify the most favourable configurations for a new system layout. The performed analysis examines each configuration with respect to its thermal-hydraulic performance to assess the power uprate potential which is limited by multiple temperature constraints (e.g. fuel centreline and cladding temperatures as well as the coolant freezing/boiling). The estimated maximum power was then used in the fuel burnup calculations, from which a discharge burnup and cycle average Coolant Temperature Coefficients (CTC) were obtained. As a result of the optimisation process, several families of possible solutions were identified, which form an optimal Pareto Front. The most attractive configurations in terms of achievable power density, however, were not necessarily on the Pareto front. Several coolant options are examined and compared in this paper. The newly identified design options have the potential to achieve power density that is higher than that of a typical AGR by up to a factor of five while maintaining negative CTC through the burnup cycle. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Predicting Li-ion Battery Performance for Impurity-doped NMC Cathodes Using Deep Learning

With the electric vehicle (EV) market expansion and the energy sector's shift towards electrification, the demand for battery metals, including lithium (Li), cobalt (Co), and nickel (Ni), is set to surpass supply. A critical knowledge gap exists in the purity standards for battery precursors and the impact of impurities on battery performance. Addressing this, our study employs a Deep Machine Learning (DL) based multi-objective optimization approach to interpret the relationship between metal impurities in domestic battery resources and their effects on battery performance. We analyze experimental data from Li-ion batteries with NMC (Nickel-Manganese-Cobalt oxide) cathodes over 1000 cycles, representing approximately ~6-8 months of operation, to establish a baseline of performance without impurities. Leveraging this data, we develop a Physics-Informed Deep Learning (PIDL) framework to extend our findings to cases that include metal impurities (e.g., Fe, Cu, Al) ranging from (0.001 - 0.01) %, respectively. By incorporating physics-based features, our PIDL model can accurately estimate the performance of NMC cathodes doped with various metal impurities to provide rapid design decisions. This research paves the way for informed decisions in Li-ion battery material design and optimization, ensuring the sustainable growth of the EV market and the broader energy sector.

25 ENERGY STORAGE↗
DOE OSTI2024

CEBAF Injector for K Long Beam Conditions

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab concurrently operates four experimental Halls with distinct bunch charge specifications and repetition rates. Numerous critical beam parameters within CEBAF are configured in the injector, some remaining unchanged throughout the accelerator. Consequently, the injector plays a crucial role in determining final beam characteristics, including bunch structure, beam sizes, bunch lengths, energy spread, and beam transmission. The Jefferson Lab KL experiment is scheduled to take place at CEBAF in Hall D, featuring a much lower bunch repetition rate of 7.80 MHz or 15.59 MHz, below the nominal values of 249.5 MHz or 499 MHz. Although the proposed average current of 5 ?A or 10 ?A is low compared to the maximum CEBAF cur- rent of approximately 180 ?A, the corresponding bunch charge is unusually high for CEBAF injector operation. This study focuses on the behavior of low-repetition-rate, high-bunch- charge (0.32 to 0.64 pC) beams in the CEBAF injector. We investigated the evolution and transmission of low-charge beams to space-charge dominated high-charge beams in the front end of the CEBAF injector for two configurations: the pre-existing CEBAF Phase 1 injector upgrade, operated at 130 kV, and the existing CEBAF Phase 2 injector upgrade, operated at 140 kV, 180 kV, and to be operated at 200 kV. The electron beam through the CEBAF injector is characterized using beam dynamics simulations and comparisons with the available measurements performed at 130 kV. Multi-objective genetic optimizations of the CEBAF injector were performed to determine the operating magnetic elements and RF settings for the evolution and transmission of low, moderate, and high charge beams in the CEBAF injector at 180 kV and 200 kV DC gun voltages. Subsequently, simulations at the same voltages were conducted to obtain the beam characteristics at the front end of the CEBAF injector. The laser spot size and laser pulse length at the cathode were varied to observe their effects on beam transmission in the injector at different voltages (130 kV, 180 kV, and 200 kV). Experimental studies at 130 kV, 140 kV, and 180 kV validate the simulations. Beam study measurements are carried out using EPICS tools, while optimizations and simulations are facilitated by General Particle Tracer. Based on the findings, optimal parameters for the upcoming Jefferson Lab KL experiment are proposed, utilizing a lower repetition rate and higher bunch charge

Pokharel, Sunil↗
DOE OSTI2024

Optimization and Experimental Validation of Annular Finned PCM-HX for a Domestic Hot Water Heater Application

The load profile for domestic water heating is time-dependent and can result in high energy demand during peak operating times. Shifting this peak load can have significant environmental and economic impacts. Phase change material (PCM)-based thermal energy storage (TES) is a potentially useful technology for peak load shifting in domestic hot water (DHW) applications thanks to its high latent heat and energy density. In this study, an annular finned-tube PCM-HX design concept was optimized for a load-shifting TES unit to meet the Department of Energy standard for a medium-usage DHW heater using a resistance-capacitance model (RCM) integrated with a Multi-Objective Genetic Algorithm. The optimized design comprised 70 identical annular finned-tube PCM-HX units connected in parallel and utilizing RT62HC as the PCM. A single PCM-HX unit was prototyped and tested in a vertically oriented setup with upward heat transfer fluid (HTF) flow. The hot water supply time was defined based on a cutoff temperature of 51.7°C. The as-designed mass flow rate (1.5 g/s) was tested to assess the performance of the prototyped PCM-HX unit for RCM validation. For the experimental investigation, RTD sensor bundles measured HTF temperature at the PCM-HX inlet and outlet, and a Coriolis flow meter accurately measured the HTF mass flow rate. The simulated discharging power underpredicted the experimental result by about 12%, and the simulated hot water supply time underpredicted the experimental result by approximately 13% for the as-designed mass flow rate (1.5 g/s). The average deviation of the hot water supply temperature between the experimental and RCM results during the complete PCM solidification process was 1.3 K for the as-designed mass flow rate. The overall good agreement between the experimental and RCM results provides confidence that computationally efficient models such as RCM can be utilized for design optimization of PCM-HXs.

42 ENGINEERING↗
DOE OSTI2024

Accelerated CO2 Storage Optimization Using Multi-Resolution Fourier Neural Operator at the Illinois Basin Decatur Project (IBDP)

This paper presents a deep learning-based approach for optimizing CO2 injection in carbon capture and storage (CCS) operations. We developed a multi-resolution machine learning model to significantly reduce data generation costs. Utilizing this proxy model, we implemented a multi-objective genetic algorithm to optimize well control during the CO2 injection process. The proposed approach was applied to the Illinois Basin Decatur Project (IBDP), successfully optimizing the CO2 injection schedule based on three key objectives: maximizing the amount of CO2 stored, maximizing sweep efficiency, and minimizing pressure increase. The use of the proxy model accelerated the optimization workflow by two orders of magnitude, while the cost of data generation for the proxy model was reduced by 90% by utilizing a coarse-scale model.

accelerated CO2 storage optimization↗
DOE OSTI2025

Surrogate Neural Architecture Codesign Package (SNAC-Pack)

Neural Architecture Search is a powerful approach for automating model design, but existing methods struggle to accurately optimize for real hardware performance, often relying on proxy metrics such as bit operations. We present Surrogate Neural Architecture Codesign Package (SNAC-Pack), an integrated framework that automates the discovery and optimization of neural networks focusing on FPGA deployment. SNAC-Pack combines Neural Architecture Codesign's multi-stage search capabilities with the Resource Utilization and Latency Estimator, enabling multi-objective optimization across accuracy, FPGA resource utilization, and latency without requiring time-intensive synthesis for each candidate model. We demonstrate SNAC-Pack on a high energy physics jet classification task, achieving 63.84% accuracy with resource estimation. When synthesized on a Xilinx Virtex UltraScale+ VU13P FPGA, the SNAC-Pack model matches baseline accuracy while maintaining comparable resource utilization to models optimized using traditional BOPs metrics. This work demonstrates the potential of hardware-aware neural architecture search for resource-constrained deployments and provides an open-source framework for automating the design of efficient FPGA-accelerated models.

Weitz, Jason [UC, San Diego] (ORCID:00090004631535↗
DOE OSTI2025

Machine Learning-Driven Optimization of Building Enclosures for Moisture Durability and Thermal Performance

The design of moisture-durable building enclosures with low embodied carbon often involves an iterative process of selecting the materials for the specific exposure conditions to meet the performance requirements. While hygrothermal simulations are commonly used to evaluate moisture durability, they often require advanced expertise for proper implementation. Machine learning (ML) provides a promising alternative by streamlining the design process and minimizing the reliance on complex simulations. This study presents a machine learning-based approach for predicting moisture durability in residential wall assemblies. The ML model was trained to estimate the mold index and maximum moisture content of various layers under typical exposure conditions. The model achieved a high predictive accuracy, with a coefficient of determination (R²) exceeding 0.90 when compared to traditional hygrothermal simulations on materials that were not part of training the ML model. Building on these results, the ML model was developed into a practical tool for optimizing wall assembly designs. This tool allows users to automatically optimize material selections based on energy, moisture, and carbon performance criteria. By incorporating multi-objective optimization, the tool identifies configurations that minimize embodied carbon while maintaining moisture safety and code-compliant thermal performance. Additionally, it provides insights into how material choices influence assembly durability, energy efficiency, and carbon reduction. The tool will be implemented in the Building Science Advisor (BSA) to enhance its performance and provide more granularity on the results. This research highlights the potential for ML-driven tools to simplify the design of high-performance building enclosures, offering architects and engineers a faster, more efficient way to balance critical performance factors.

Salonvaara, Mikael [ORNL] (ORCID:0000000318991554)↗
DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on the Banshee Distribution Network: Preprint

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

DEVELOPMENT AND APPLICATION OF RISK ANALYSIS TOOLKIT FOR PLANT RESOURCE OPTIMIZATION

This paper presents the development of methods and tools that are being designed to optimize plant operations (e.g., maintenance/replacement schedules and optimal maintenance postures for plant components) in a manner that is more cost effective than current approaches and makes better use of available component health and cost data. These methods include both data- and model-based optimization methods. Model-based optimization methods directly include reliability and cost models to determine an optimal plant operational strategy. We consider gradient-based and evolutionary (based on genetic algorithms) optimization methods. The second class of methods target more specific use cases (e.g., project schedule optimization) and are not based on reliability models directly, but they require specific component reliability and cost data. This class of methods is based on variants of the knapsack problem with an aim to determine an optimal project schedule that maximizes the overall NPV. This paper also presents multi-objective methods designed to identify an optimal maintenance posture based on a Pareto frontier analysis. Rather than dictating the “right” tradeoff (i.e., identify the absolute best posture), we show how it is possible to perform a trade space exploration approach (i.e., identify value and costs of several postures and let the analysis account for desired value and cost metrics). This is performed by identifying maintenance postures that maximize value (e.g., system availability) and minimize operational costs, i.e., the Pareto frontier in a value-cost trade space. For all these methods we present detailed applicative examples that show their validity from a decision-making perspective.

97 - MATHEMATICS AND COMPUTING↗