A Descriptive Model for Determining Optimal Human Performance in Systems, Volume 1
Man machine development cycle and mathematical models for optimal human performance
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SEARCH · Engineering Papers
Results for “Model optimization”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Techno-economic optimization of renewable urea production for sustainable agriculture and CO 2 utilization
Abstract Urea is the most used nitrogen fertilizer due to its ease of storage, transportation, and application. It is made by combining ammonia and carbon dioxide (CO 2 ), both of which are produced predominantly from fossil fuels at present. The recent momentum behind ammonia production using renewable-powered electrolysis offers an opportunity to both make urea in a more sustainable way and utilize CO 2 from external sources. In this work, we present a techno-economic optimization model to minimize the cost of making urea in this way. The model allows for time-varying chemical production in response to renewable variability by simultaneously optimizing production facility design and hourly operation. We performed a case study for Minnesota considering the use of byproduct CO 2 from bioethanol production. We found that the present-day levelized cost of renewable urea is between $268 mt −1 and $413 mt −1 at likely implementable production scales up to 250 000 mt yr −1 . This is within the range of historical conventional urea prices while offering at least 78% carbon intensity reduction. Projecting to 2030, there is a clear economic case for renewable urea production with levelized cost as low as $135 mt −1 due to technology improvement and electrolysis manufacturing expansion, facilitating a urea production scale increase to 525 000 mt yr −1 . Optimal facilities use wind energy, with hydrogen and ammonia production operating in a flexible, time-varying way to minimize battery and hydrogen storage capacities. Urea production operates near steady state due to the relatively low cost of intermediate ammonia buffer storage. A mix of imported methane and locally produced hydrogen are used to provide heat for steam consumed in the urea synthesis.
Chemistry↗
Mixture-of-Experts for Multi-Domain Defect Identification in Non-Destructive Inspection
Composite materials are widely used in aircraft structures because of their superior mechanical properties. However, their complex failure modes require sophisticated inspection methods to ensure structural integrity. Ultrasonic testing (UT) is a common non-destructive inspection (NDI) technique for aircraft composites that can detect internal and external defects with high resolution and accuracy. Despite their effectiveness, traditional UT methods rely on the manual interpretation of ultrasonic signals, which is time-consuming, labor-intensive, and subjective. Furthermore, processing such large-scale data, particularly across materials of varying thicknesses, significantly increases the computational demands of deep learning model optimization. To overcome these challenges, we propose an efficient sparse mixture-of-experts (MoE) model with a multi-level loss function and introduce four novel training objectives to improve computational efficiency and accuracy in identifying surface defects in composite aircraft materials. Here, we evaluated our approach on material with multiple thicknesses or domains comprising various defects. Our experimental results demonstrate higher accuracy and F1-Score, with only 10% training epochs compared to baseline MoE.
composite materials↗
Prediction of the development of islet autoantibodies through integration of environmental, genetic, and metabolic markers
The Environmental Determinants of the Diabetes in the Young (TEDDY) study has prospectively followed, from birth, children at increased genetic risk of type 1 diabetes. We evaluated the potential of machine learning to identify new biomarkers that predict imminent (within 6 months) development of persistent islet autoantibodies to insulin, GAD or IA-2 in TEDDY participants through integration of time-invariant risk factors with time-varying metabolomics. The predictive modeling was initiated with over 220 potential biomarkers; through ensemble-based feature evaluation, the optimal model included 42 biomarkers, returning a cross-validated receiver operating characteristic area under the curve of 0.74. The model identified a principal set of 20 time-invariant markers, including 16 single nucleotide polymorphisms and two HLA-DR genotypes, gestational age, and exposure to a prebiotic formula. Integration of the metabolome identified 22 high-priority metabolites and lipids, including adipic acid and ceramide d42:0, that predicted development of islet autoantibodies, dependent upon the time horizon. The majority (86%) of metabolites that predicted development of islet autoantibodies belonged to 3 pathways: lipid oxidation, phospholipase A2 signaling, and pentose phosphate pathway. TEDDY data suggest that these metabolic processes may play a role in triggering islet autoimmunity.
59 BASIC BIOLOGICAL SCIENCES↗
The Applications of NASA Mission Technologies to the Greening of Human Impact
I will give an overview talk about flight software systems, robotics technologies and modeling for energy minimization as applied to vehicles and buildings infrastructures. A dominant issue in both design and operations of robotic spacecraft is the minimization of energy use. In the design and building of spacecraft increased power is acquired only at the cost of additional mass and volumes and ultimately cost. Consequently, interplanetary spacecrafts are designed to have the minimum essential power and those designs often incorporate careful timing of all power use. Operationally, the availability of power is the most influential constraint for the use of planetary surface robots, such as the Mars Exploration Rovers. The amount of driving done, the amount of science accomplished and indeed the survivability of the spacecraft itself is determined by the power available for use. For the Mars Exploration Rovers there are four tools which are used: (1) models of the rover and it s thermal and power use (2) predictive environmental models of power input and thermal environment (3) fine grained manipulation of power use (4) optimization modeling and planning tools. In this talk I will discuss possible applications of this methodology to minimizing power use on Earth, especially in buildings.
Sims, Michael H.↗
NASA Tech Briefs, February 2001
The topics include: 1) Application Briefs; 2) National Design Engineering Show Preview; 3) Marketing Inventions to Increase Income; 4) A Personal-Computer-Based Physiological Training System; 5) Reconfigurable Arrays of Transistors for Evolvable Hardware; 6) Active Tactile Display Device for Reading by a Blind Person; 7) Program Automates Management of IBM VM Computer Systems; 8) System for Monitoring the Environment of a Spacecraft Launch; 9) Measurement of Stresses and Strains in Muscles and Tendons; 10) Optical Measurement of Temperatures in Muscles and Tendons; 11) Small Low-Temperature Thermometer With Nanokelvin Resolution; 12) Heterodyne Interferometer With Phase-Modulated Carrier; 13) Rechargeable Batteries Based on Intercalation in Graphite; 14) Signal Processor for Doppler Measurements in Icing Research; 15) Model Optimizes Drying of Wet Sheets; 16) High-Performance POSS-Modified Polymeric Composites; 17) Model Simulates Semi-Solid Material Processing; 18) Modular Cryogenic Insulation; 19) Passive Venting for Alleviating Helicopter Tail-Boom Loads; 20) Computer Program Predicts Rocket Noise; 21) Process for Polishing Bare Aluminum to High Optical Quality; 22) External Adhesive Pressure-Wall Patch; 23) Java Implementation of Information-Sharing Protocol; 24) Electronic Bulletin Board Publishes Schedules in Real Time; 25) Apparatus Would Extract Water From the Martian Atmosphere; 26) Review of Research on Supercritical vs Subcritical Fluids; 27) Hybrid Regenerative Water-Recycling System; 28) Study of Fusion-Driven Plasma Thruster With Magnetic Nozzle; 29) Liquid/Vapor-Hydrazine Thruster Would Produce Small Impulses; and 30) Thruster Based on Sublimation of Solid Hydrazine
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Optimization of the Roll-Out Solar Array (ROSA) and Mega-ROSA
Phase II Objectives: Optimize Mega-ROSA/ROSA solar array materials and structures; Characterize creep/relaxation phenomena; Perform analytical modeling; Optimize the array's manufacturing process
Spence, Brian↗
Energy storage in combined gas-electric energy transitions models: The case of California
California’s vision for a net-zero future by 2045 relies heavily on variable renewable energy systems. Thus, energy storage - particularly long-duration storage - could play a fundamental role in reliably supplying low-carbon electricity. We study energy storage using the BRIDGES model, a combined gas-electric capacity expansion model for California across multiple investment periods (2025-2045), modeled with progressively decreasing carbon emission targets to a zero emissions by 2045. This least-cost optimization model includes renewable gas production via power-to-gas, long-term storage of energy in gaseous form, electric energy storage such as through batteries and hydrogen storage, and renewable energy generation, all with capacity tracking and investment. Multiple scenarios are evaluated to examine the sensitivity of the optimal storage portfolio to system-level and sector-level parameters. The scenario results show that all electric energy storage systems - which vary in storage duration - are deployed and required in a net-zero California in 2045, amounting to around 75 GW of storage capacity. Lithium ion systems make up approximately 80% of this power capacity and supply most short-run storage needs. Hydrogen storage - in the form of a power-to-gas-to-power system - emerges as a replacement to conventional natural gas storage, comprising most of the total energy storage capacity (~ 4 TWh). This capacity is less than 5% of the current natural gas storage capacity (94 TWh), indicating sufficient room for repurposing part of the gas infrastructure. A demand-side sensitivity analysis proves that higher electricity demand correlates with more builds of Li-ion batteries, while higher industrial heat demand leads to more builds of long-duration storage systems in a net-zero economy. Furthermore, power-to-gas systems satisfy part of the industrial heat demand by locally supplying renewable gas, which overtakes the traditional centralized gas storage and transfers through pipelines, casting significant doubts on the future of the large-scale gas infrastructure.
03 NATURAL GAS↗
Inference of phase field fracture models
The phase field approach to modeling fracture uses a diffuse damage field to represent cracks. This representation mollifies singularities that arise in computations with sharp interface models and some of the resultant difficulties in the mathematical and numerical treatment of fracture. Phase field fracture models have proven effective at representing crack propagation, branching, and merging. Specific formulations, beginning with brittle fracture, have also been shown to converge to classical solutions. Extensions to cover the range of material failure, including ductile and cohesive fracture, lead to an array of possible models. There exists a large body of literature focusing on this class of models and on the impact of model form on the predicted crack evolution. However, there have not been systematic studies into how optimal models may be chosen. Here, we take a first step in this direction by developing formal methods for identification of the best parsimonious model of phase field fracture given full-field data on the damage and deformation fields. We consider some of the main models that have been used for the degradation of elastic response due to damage and its propagation. Our approach builds upon Variational System Identification (VSI), a weak form variant of the Sparse Identification of Nonlinear Dynamics (SINDy). Furthermore, in this first communication we focus on synthetically generated data but we also consider central issues associated with the use of experimental full-field data, such as data sparsity and noise.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Development of Steady-State and Dynamic Mass and Energy Constrained Neural Networks for Distributed Chemical Systems Using Noisy Transient Data
The paper presents the development of algorithms for mass and energy constrained neural network models that can exactly conserve the overall mass and energy of distributed chemical process systems, even though the noisy transient data used for optimal model training violate the same. In contrast to approximately satisfying mass and energy balance constraints of a system by soft penalization of objective function, algorithms have been developed for solving equality-constrained nonlinear optimization problems, thus providing the guarantee of exactly satisfying the system mass and energy conservation laws. For developing dynamic mass-energy constrained network models for distributed systems, hybrid series and parallel dynamic-static neural networks have been leveraged. The developed algorithms for solving both the training and forward problems are validated using both steady-state and dynamic data in the presence of various noise characteristics. The developed data-driven algorithms are flexible to exactly satisfy mass and energy balance constraints for dynamic chemical processes if the system holdup information is available. The proposed network structures and algorithms are applied to the development of data-driven lumped and distributed models of an adiabatic superheater/reheater system, a nonisothermal continuous stirred tank reactor, as well as an electrically heated plug-flow reactor system where one form of energy gets transformed to another. It has been observed that the mass-energy constrained neural networks yield a root mean squared error of <1% with respect to the system truth for the case studies evaluated in this work.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
The value of in-reservoir energy storage for flexible dispatch of geothermal power
Geothermal systems making use of advanced drilling and well stimulation techniques have the potential to provide tens to hundreds of gigawatts of clean electricity generation in the United States by 2050. With near-zero variable costs, geothermal plants have traditionally been envisioned as providing “baseload” power, generating at their maximum rated output at all times. However, as variable renewable energy sources (VREs) see greater deployment in energy markets, baseload power is becoming increasingly less competitive relative to flexible, dispatchable generation and energy storage. Herein we conduct an analysis of the potential for future geothermal plants to provide both of these services, taking advantage of the natural properties of confined, engineered geothermal reservoirs to store energy in the form of accumulated, pressurized geofluid and provide flexible load-following generation. We develop a linear optimization model based on multi-physics reservoir simulations that captures the transient pressure and flow behaviors within a confined, engineered geothermal reservoir. We then optimize the investment decisions and hourly operations of a power plant exploiting such a reservoir against a set of historical and modeled future electricity price series. Further, we find that operational flexibility and in-reservoir energy storage can significantly enhance the value of geothermal plants in markets with high VRE penetration, with energy value improvements of up to 60% relative to conventional baseload plants operating under identical conditions. Across a range of realistic subsurface and operational conditions, our modeling demonstrates that confined, engineered geothermal reservoirs can provide large and effectively free energy storage capacity, with round-trip storage efficiencies comparable to those of leading grid-scale energy storage technologies. Optimized operational strategies indicate that flexible geothermal plants can provide both short- and long-duration energy storage, prioritizing output during periods of high electricity prices. Sensitivity analysis assesses the variation in outcomes across a range of subsurface conditions and cost scenarios.
15 GEOTHERMAL ENERGY↗
Mathematical model predictions and optimization study of the gamma ray atmospheric density sensor
Mathematical model predictions and optimization study of gamma ray atmospheric density sensor
Gardner, R. P.↗
Optimal nonlinear dynamic sparse model selection and Bayesian parameter estimation for nonlinear systems
Not provided.
Computer Science↗
Lithium-Ion Battery Life Model with Electrode Cracking and Early-Life Break-in Processes
This paper develops a physically justified reduced-order capacity fade model from accelerated calendar- and cycle-aging data for 32 lithium-ion (Li-ion) graphite/nickel-manganese-cobalt (NMC) cells. The large data set reveals temperature-, charge C-rate-, depth-of-discharge-, and state of charge (SOC)-dependent degradation patterns that would be unobserved in a smaller test matrix. Model structure is informed by incremental capacity analysis that shows loss of lithium inventory and cathode-material loss as the dominant capacity fade mechanisms. The model includes terms attributable to solid-electrolyte interface (SEI) growth, electrode cracking, cycling-driven acceleration of SEI growth, and "break-in" mechanisms that slightly decrease or increase available Li inventory early in life. The study explores what mathematical couplings of these mechanisms best describe calendar aging, cycle aging, and mixed calendar/cycle aging. Various approaches are discussed for extracting relevant stress factors from complex cycling profiles to predict lifetime during real-world battery loads using models trained on constant-current laboratory test results. The complexity of the present human-driven model identification process motivates future work in machine learning to more widely search and statistically discern the optimal model that correctly extrapolates capacity fade based on physical knowledge.
25 ENERGY STORAGE↗
Cooperative Research and Development Agreement between National Energy Technology Laboratory and Korea Institute of Energy Research [Abstract]
The Korea Institute of Energy Research (KIER) and the National Energy Technology Laboratory (NETL) intend to collaborate under a CRADA to advance the development of oxy-combustion technology, with an emphasis on process modeling, optimization, and techno-economic analysis. This collaboration will result in the development of a full-scale process model of a commercial oxy-fueled circulating fluid bed combustion (oxy-CFBC) plant, informed from pilot-scale data and built using the Institute for the Design of Advanced Energy Systems (IDAES) process systems engineering framework. It will also generate cost comparisons of the optimized oxyCFBC process with an air-CFBC process with and without carbon capture and sequestration.
20 FOSSIL-FUELED POWER PLANTS↗
Multiphysics Modeling of Hydrokinetic Turbine Energy Conversion System
Hydrokinetic turbines (HKTs) hold great promise as a renewable energy source, but high maintenance costs and limited energy output hinder their widespread adoption. The lack of comprehensive research on HKT drivetrain designs creates a knowledge gap in enhancing generation efficiency and cost reduction. A model of the HKT system with a focus on electric drivetrains and power converters is required to address this knowledge gap. This paper first introduces a MATLAB-averaged model integrating electrical-mechanical-thermal domains and aging behaviors within multi-time frames. Then a PLECS model, which incorporates maximum power point tracking and dq reference framed control for AC-DC-AC power converters, is enriched by a dynamic thermal model to predict fast transients accurately. These models optimize the design at the component level, resulting in improved integrated system performance. Furthermore, the validation of the models is carried out through hardware experiments for the averaged model and hardware-in-the-loop testing for the dynamic model.
ADVANCED PROPULSION SYSTEMS,HYDRO ENERGY↗
MG-RAVENS: Working Group Update [Slides]
Problem Statement: Users in industry, who are planning the deployment and operation of microgrids, face a multi-domain problem that requires multiple engineering tools to solve; Existing tools lack interoperability, which requires tedious recreation and conversion of equipment models, and creates opportunities for errors in translation or through inconsistent assumptions; Microgrid modelers waste time reinventing the wheel because it is challenging to reuse existing distribution, load, generation, power flow, optimization models for new use cases.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Multiple aspects maintenance ontology-based intelligent maintenance optimization framework for safety-critical systems
Abstract Maintenance optimization is a process for improving the efficiency of maintenance strategies and activities, considering various aspects of the target system and components, such as the probabilities of system failures and the cost of repair and replacement of a failed component. The improvement of maintenance optimization algorithms generally requires information from various data sources. For example, it may require the system risk information derived from risk analysis tools or the residual lifetime of a component from fault prognosis tools. The requirements of data acquisition (DAQ) and aggregation pose new challenges for maintenance management systems (MMSs) that implement and use these maintenance optimization algorithms. This paper proposes a multiple aspects maintenance ontology-based framework to facilitate DAQ from MMSs, online monitoring systems, fault detection and discrimination tools, risk assessment tools, decision-making tools, and component identification tools, and accelerate the implementation and verification of contemporary maintenance optimization models and algorithms. The proposed framework consists of a multi-aspect maintenance ontology with critical information for maintenance optimization and application interfaces for collecting information from various data sources, such as fault prognosis tools, online monitoring tools, risk assessment tools, and decision-making algorithms. In addition, this paper proposes a heuristic method for integrating concepts and properties from other existing ontologies into the proposed framework when the existing ontology is not fully compatible with the ontology under construction. Finally, the paper verifies the proposed ontology framework using a feedwater system designed for nuclear power plants with valves and filters as the components under maintenance.
Diao, Xiaoxu (ORCID:0000000346726352)↗