Results for “strategy optimization” — Page 41

NASA NTRSDate not supplied

A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling

Urban Air Mobility (UAM) promises to revolutionize transportation in major cities, offering passenger travel, cargo delivery, and emergency medical services through a network of electric vertical takeoff and landing (eVTOL) aircraft. However, the limited range of current eVTOLs, due to the low specific energy of lithium-ion batteries along with a possibility of thermal runaway conditions poses significant safety concerns, leading to potentially compromising operational safety. To address this critical challenge, researchers are actively evaluating the impact of flight and environmental conditions on onboard lithium-ion battery health. This involves carefully assessing the performance of battery packs under laboratory and operational conditions for developing models to estimate future health using prognostics framework. This study examines the effectiveness of evaluating battery degradation leading to catastrophic failures under varying operational conditions in laboratory. These are captured using physics based models of underlying phenomenons and integrated into the prognostics framework. A fully charged battery undergoes controlled discharge cycles at varying C-rates based on the simulated power draw profile, with current and voltage, temperature data recorded throughout the experiment. The observed data provides valuable insights into how different operating conditions and mission profiles affect battery performance. This information is crucial for developing strategies to optimize battery systems, enhance range, and ultimately ensure the safe and reliable operation of UAM vehicles.

Thermal Modeling↗

NASA NTRSDate not supplied

A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling

Urban Air Mobility (UAM) promises to revolutionize transportation in major cities, offering passenger travel, cargo delivery, and emergency medical services through a network of electric vertical takeoff and landing (eVTOL) aircraft. However, the limited range of current eVTOLs, due to the low specific energy of lithium-ion batteries along with a possibility of thermal runaway conditions poses significant safety concerns, leading to potentially compromising operational safety. To address this critical challenge, researchers are actively evaluating the impact of flight and environmental conditions on onboard lithium-ion battery health. This involves carefully assessing the performance of battery packs under laboratory and operational conditions for developing models to estimate future health using prognostics framework. This study examines the effectiveness of evaluating battery degradation leading to catastrophic failures under varying operational conditions in laboratory. These are captured using physics based models of underlying phenomenons and integrated into the prognostics framework. A fully charged battery undergoes controlled discharge cycles at varying C-rates based on the simulated power draw profile, with current and voltage, temperature data recorded throughout the experiment. The observed data provides valuable insights into how different operating conditions and mission profiles affect battery performance. This information is crucial for developing strategies to optimize battery systems, enhance range, and ultimately ensure the safe and reliable operation of UAM vehicles.

Thermal Modeling↗

NASA NTRSDate not supplied

A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling

Urban Air Mobility (UAM) promises to revolutionize transportation in major cities, offering passenger travel, cargo delivery, and emergency medical services through a network of electric vertical takeoff and landing (eVTOL) aircraft. However, the limited range of current eVTOLs, due to the low specific energy of lithium-ion batteries along with a possibility of thermal runaway conditions poses significant safety concerns, leading to potentially compromising operational safety. To address this critical challenge, researchers are actively evaluating the impact of flight and environmental conditions on onboard lithium-ion battery health. This involves carefully assessing the performance of battery packs under laboratory and operational conditions for developing models to estimate future health using prognostics framework. This study examines the effectiveness of evaluating battery degradation leading to catastrophic failures under varying operational conditions in laboratory. These are captured using physics based models of underlying phenomenons and integrated into the prognostics framework. A fully charged battery undergoes controlled discharge cycles at varying C-rates based on the simulated power draw profile, with current and voltage, temperature data recorded throughout the experiment. The observed data provides valuable insights into how different operating conditions and mission profiles affect battery performance. This information is crucial for developing strategies to optimize battery systems, enhance range, and ultimately ensure the safe and reliable operation of UAM vehicles.

Thermal Modeling↗

NASA NTRSDate not supplied

Assessing the Relationships Between Sensorimotor Biomarkers and Post-Landing Functional Task Performance

Spaceflight drives adaptive changes in healthy individuals appropriate for sensorimotor function in a microgravity environment. These changes are maladaptive for return to earth's gravity. The inter-individual variability of sensorimotor decrements is striking, although poorly understood. The goal of this study was to identify a set of behavioral, neuroimaging and genetic measures that can be used to predict early post-flight performance on a set of sensorimotor tasks. Astronauts were recruited who previously participated in sensorimotor field tests and/or posturography soon after long-duration spaceflight. Behavioral tests included assessments of sensory dependency and adaptability. Visual dependency involved treadmill walking while viewing a moving virtual visual scene. Vestibular perceptual thresholds were measured while seated during lateral translations. Proprioception dependency was measured during one-legged stance on a horizontal air-bearing surface. Ground assessment of adaptability was performed (1) during treadmill walking with a virtual linear hallway and a moving walking surface, and (2) during multiple trials of navigating an obstacle course while wearing reversing prisms (adaptive Functional Mobility Test, aFMT). The neuroimaging tests characterized individual differences in regional brain volumes (using Structural MRI) and white matter microstructure (using Diffusion Tensor Imaging) to serve as potential predictors of adaptive capacity. The genetic tests utilized saliva samples to examine variations in four genes chosen because of their ability to differentiate sensorimotor adaptation ability in a normative population, including Catechol-O-methyltransferase (COMT), Dopamine Receptor D2 (DRD2), Brain-derived neurotrophic factor (BDNF) and the α2-adrenergic receptor. Thirty ISS crewmembers were recruited, including 12 first-time fliers, 6F, and mission durations lasting 185.5 ± 45.5 days, mean ± std. We utilized a combination of three post-flight functional task outcomes: tandem walk, recovery from fall and dynamic posturography. There was considerable variability among all post-flight performance outcomes. Based on an ordinal scale survey, 72% indicated their ability to perform functional tasks were more impacted postflight relative to inflight with 50% indicating they needed to restrict movements for a longer period postflight relative to inflight. While there is a strong association within tests obtained at different R+0 timepoints, by R+24 hr performance on one post-flight test does not necessarily correlate with performance on other post-flight tests. Preliminary statistical analysis indicates combining biomarkers will increase predictive power and this will be explored with future analyses. Our preliminary findings underscore the importance of a comprehensive post-flight test battery including different types of tasks with varying sensory feedback. Understanding the relationships between these sensorimotor biomarkers and post-flight functional task performance improve both our understanding of the individual variability and our strategy to optimize sensorimotor countermeasures.

biomarkers↗

DOE OSTI2020

Appropriations and Financing: Strategic Use of Funding for Performance Contracts

42 U.S.C. § 8253 Sections 432 and 512 (EISA of 2007) authorize agencies to use appropriations, private financing, or a combination of appropriations and private financing to comply with its requirements. With this flexibility in mind, federal agencies should evaluate funding options early and employ a strategy that optimizes the impact of existing appropriations and financing options. Maximize value to government through strategic use of appropriations and energy savings, an optimal set of infrastructure improvements that meet site needs and program objectives, and an actionable plan for sustained performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

BETO 2021 Peer Review - Rational Design of Robust Reactor Feeding Systems for Heterogeneous Cellulosic and Agricultural Wastes Based on Biomass Quality Characteristics

Consistent and reliable preprocessing, conveyance, and reactor in-feed systems, particularly for low-cost waste feedstocks, remains a major technical challenge for the emerging Bioeconomy. By identifying critical biomass attributes and connecting them to flow and conversion behavior, science-driven system designs can address these often-overlooked solids handling challenges. The Wonderful Company (TWC) is the world's largest almond and pistachio grower, generating 250,000 dry tons/year of waste material including hulls, shells, and wood (>5 million tons/year industry wide in the U.S.). This project seeks to turn this environmental and economic liability into a sustainable and profitable resource, targeting conversion via gasification to syngas for electricity and bio-char, by addressing related material handling and feeding challenges. Based on optimized preprocessing strategies, bulk material flow, and thermal conversion properties, an overall system design will be developed. The methodology will be tested with FCIC's benchmark loblolly pine residues, to demonstrate the robustness of the overall approach and provide insight and guidance for future systems. The project will culminate in extended field trials to demonstrate an improved continuous feeding system with a commercial biomass-to-electricity gasifier vendor, and an economic analysis demonstrating a reduction in electricity production costs by maximizing on-stream time while minimizing preprocessing and CapEx costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

A Simplified Building Modeling Approach for Identifying Whole Building Retrofits

Building Energy Modeling (BEM) is an effective strategy for optimizing new and retrofit building design, evaluating its energy savings potential and rating its energy performance. However, because of the complexity and expense of modeling, only a small percentage of buildings are simulated. Over 80% of buildings are <25,000 ft2, and the cost associated with BEM can be a big deterrent for reaching this subsector, which usually ends up complying with energy codes and qualifying for incentives using prescriptive measures or generalized design guides. Similarly, efficiency considerations for small building retrofits are often identified through deemed measures or Technical Reference Manuals, which though a scalable and convenient approach, can limit innovation in design and optimization of measures. Simplification of the modeling process, where appropriate, can lead to increased energy savings and more informed decision making. This paper will discuss the development of a ruleset for a simplified approach based on the Performance Rating Method (PRM) contained in ASHRAE Standard 90.1. The approach aims to simplify and lower the cost of the energy modeling process in a controlled and documented manner. An anticipated outcome is an increased use of BEM and its effectiveness in the design, retrofit and operation of commercial buildings. In addition, simplification of the modeling process will result in fewer errors from misinterpretations of both program requirements and program intent in simulation software. The paper will discuss the need and advantages of a simplified modeling approach for whole building retrofits and the technical process for achieving the same.

Goel, Supriya↗

DOE OSTI2022

Pellet Cladding Interaction In-Reactor Ramp Testing in a World without the Halden Boiling Water Reactor

One of the most crucial performance areas for fuel rods in water cooled nuclear power plants is interaction between cladding tubes and fuel pellets. Experimental programs in test reactors have provided key data to help fuel developers and plant operators understand Pellet Cladding Interaction (PCI) phenomena and optimize their strategies for reliable fuel performance. Approximately 50 years of “ramp” testing programs, where the fission heating rate is deliberately manipulated in test rods, have been performed in a handful of test reactors to reveal and understand PCI behaviors such as iodine-assisted stress corrosion cracking. Unfortunately, the test reactors most engaged in this type of work have all been retired over the years up to the recent and unexpected closure of the Halden Boiling Water Reactor which effectively caused a hiatus in PCI ramp testing programs. The need for ramp testing is crucial at this time to enable refined PCI understanding as more plants consider implementing flexible operations, increased fuel rod burnup limits, and new fuel technologies with enhanced accident tolerance. This paper reviews some of the key PCI phenomena that must be addressed with in-pile testing and surveys past test reactor’s methods for achieving the needed conditions. A strategic approach is then presented using test reactors and complimentary facilities which are still available today. Near term data opportunities are put forth along with capability development strategies that will ensure future longevity in this field of research.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

CRITICAL FUNCTION ASSURANCE: Understanding Critical Function and Critical Function Delivery is Foundational for Meaningful ICS Security Improvement and Policy Efforts

Modern life is enabled by a complex and interdependent web of critical functions, including energy, communications, transportation, food, and water. Automation has significantly reduced or replaced human interactions in the delivery of these functions, resulting in a web of goods and services that are made available 24/7 only through unique and intentional deployments of microprocessors, software, and firmware technologies. The prospect of cyber-enabled sabotage of these processes disrupts traditional risk determination models. Critical Function Assurance (CFA) is a foundational approach to identifying, prioritizing, and mitigating the risk that is inherent in the delivery of critical functions that depend on digital technology. It provides rapid focus to what matters most and illuminates elements and areas of risk that otherwise are often overlooked. This focus enables effective application of available security resources and optimizes security strategy and policy efforts. This paper introduces CFA to decision makers and risk executives (including CEOs, COOs, CFOs, and CISOs) whose organizations support and deliver the critical functions that underpin national defense, societal health and safety, and a vibrant economy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Fracture Networks Imaging in CO2 Injection Zones in IBDP Site: An Unsupervised Machine Learning Application with Multiple Datasets

This is the conference paper accompanying a poster presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24 , 2024. This work highlights the integration of unsupervised machine learning (ML) techniques as a transformative tool for advancing understanding of CO2 injection into reservoirs that could potentially contribute to optimizing injection strategies and reservoir management, ultimately bolstering the efficacy and sustainability of CO2 storage.

Kumar, Abhash↗

DOE OSTI2025

Techno-Economic Assessments of Second-Life Batteries for Electric Vehicle Charging Stations

When electric vehicle (EV) batteries degrade below a certain capacity, they may no longer be suitable for automotive use but can be repurposed as second-life batteries (SLBs) for other applications, such as EV charging stations. When integrated with photovoltaic (PV) systems, SLB can store surplus solar energy, reducing reliance on the grid and lowering operational costs. This paper presents a novel techno-economic assessment framework for deploying SLBs in combination with PV in grid-connected EV charging stations. The proposed framework integrates the value proposition, charging station operation, optimal dispatch strategies, battery degradation modeling, input data requirements, and detailed procedures for generating key economic performance metrics. Insightful analyses are performed to assess the performance of SLBs in comparison to new batteries across various cost scenarios. The results indicate that SLBs become financially attractive when their cost is 40% or lower than new batteries.

energy storage↗

DOE OSTI2024

Individual Data Sparsity in Smart Thermostat Big Data: Impacts on Modeling Thermostat Use Behavior Dynamics

This study explores the impacts of the sparsity of individual thermostat interaction data on modeling thermostat use behavior dynamics using a dataset of over 100,000 smart thermostats. In developing a data-driven model of Thermal Frustration Theory (TFT), we investigate the challenges and trade-offs in clustering occupant data to enhance predictive accuracy. Our findings reveal that a single, aggregated model fails to capture the diversity of occupant behaviors, resulting in extremely poor prediction performance. Conversely, excessive clustering exacerbates data sparsity, undermining model reliability. By identifying an optimal clustering strategy, we achieve a balance that significantly improves the prediction of manual setpoint changes during demand response (DR) events, enhancing energy management and occupant comfort

Fannon, David↗

DOE OSTI2025

Predictive Modeling and Uncertainty Quantification in Condition Monitoring of Active Components: A Reactor Coolant Pump Use Case

This work develops data-driven models for onset of thermal barrier leakage in reactor coolant pumps. It incorporates uncertainty quantification to enhance the reliability and robustness of pre- dictions. Using synthetic data generated by the Generic Pressurized Water Reactor simulator, realistic degradation scenarios were simulated across lifecycle stages—beginning, middle, and end of life. Key variables, including differential pressure, flow rate, vibration, and temperatures, were analyzed using machine learning framework. The fully connected neural network models demonstrated exceptional performance, achieving R2 scores exceeding 0.99 and root mean square errors as low as around 8.23 × 10-2 gallon per minute (gpm) for the three stages of the lifecy- cle. UQ analysis further validated the model’s robustness, with narrow uncertainty bounds during steady-state operations and appropriately wider bounds during transitional phases, reflecting the physical behavior of the system. This work addresses important gaps in real-time condition moni- toring and regulatory compliance by integrating advanced condition monitoring technologies with UQ into IST programs. The ability to detect thermal barrier leakage early and quantify prediction reliability supports optimizing maintenance strategies while ensuring nuclear power plants’ safe and reliable operation.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

PSA 2025 DPRA for Cyber Optimization

Cyberattacks can have many different attack paths, durations, and goals. There are also many different mitigation options involving hardware, software, and/or humans. Evaluating defense options should include quantitative evaluation of overall effectiveness to make cost and risk-informed decisions. Typical cyberattack modeling methods only provide a qualitative evaluation and have difficulty with time dependent scenarios. The main areas of cybersecurity are confidentiality, integrity, and availability. For companies with cyber-physical systems such as advanced nuclear reactors, cyber-related integrity is a requirement set by the U.S. Nuclear Regulatory Commission. But companies are also concerned about availability or reliability as a business case. As cyber threats are evolving to a business-for-hire structure, more attacks focus on disrupting business success and reliability, causing financial and economic stability risk. Companies want reliability analysis while optimizing cost, which requires more than safety modeling methods. Dynamic-state-based and Markov-based modeling provides a method for better cyber scenario modeling with timing and conditional features not found in other numerical evaluation methods. EMRALD (Event Modeling Risk Assessment using Lined Diagrams) is a dynamic risk analysis modeling and simulation tool and has features that reduce modeling issues such as state-base explosion found in Markov-based tools. It has been used to model different time-dependent events including plant behavior and operator procedures. As a general modeling tool, EMRALD can also be used to model cyberattack scenarios with varying mitigation options and quantify effectiveness, producing numerical data for risk-informed decisions. This paper uses EMRALD to demonstrate that dynamic risk analysis can be used for cyber threat modeling to provide insights for design decision-making and optimize defense strategies.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Supporting cost-effective watershed management strategies for Chesapeake Bay using a modeling and optimization framework

Extensive efforts to adaptively manage nutrient pollution rely on Chesapeake Bay Program’s (Phase 6) Watershed Model, called Chesapeake Assessment Scenario Tool (CAST), which helps decision-makers plan and track implementation of Best Management Practices (BMPs). We describe mathematical characteristics of CAST and develop a constrained nonlinear BMP-subset model, software, and visualization framework. This represents the first publicly available optimization framework for exploring least-cost strategies of pollutant load control for the United States’ largest estuary. The optimization identifies implementation options for a BMP subset modeled with load reduction effectiveness factors, and the web interface facilitates interactive exploration of >30,000 solutions organized by objective, nutrient control level, and for ~200 counties. We assess framework performance and demonstrate modeled cost improvements when comparing optimization-suggested proposals with proposals inspired by jurisdiction plans. Stakeholder feedback highlights the framework’s current utility for investigating cost-effective tradeoffs and its usefulness as a foundation for future analysis of restoration strategies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Viral Dynamic Models During COVID‐19: Are We Ready for the Next Pandemic?

Mathematical models have been used for about 30 years to improve our understanding of virus-host interaction, in particular during chronic infections. During the COVID-19 pandemic, these models have been used to provide insights into the natural history of acute SARS-CoV-2 infection, optimize antiviral treatment strategies, understand factors associated with transmission, and optimize surveillance systems. The impact of modeling has been accelerated by the availability of unprecedented multidimensional immune data from animal and human systems, which enhanced partnerships between experimentalists and theorists and led to exciting new modeling and statistical developments. In this mini review, we examine the lessons learned from the COVID-19 pandemic and discuss the main insights provided by mathematical models of viral dynamics at the different stages of the outbreak. Although we focus on respiratory infection, we also consider the new areas for development in anticipation of future acute infections from new or reemerging pathogens.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Online task-space motion control for positioner-coordinated multi-robot manufacturing systems

Incorporating multiple robotic manipulators into large-scale manufacturing systems enhances production efficiency and expands manufacturing capabilities beyond those of single-robot systems. Workpiece positioners in robotic manufacturing have demonstrated significant benefits for process optimization, but coordination strategies for multi-robot systems with shared positioners have received limited attention. This work presents a task-space coordinated trajectory-tracking control framework for multi-robot manufacturing systems, in which robots coordinate their motions within a shared, dynamic workpiece positioning frame. A workpiece positioner actively adjusts the pose of the manufactured component to enable greater operational concurrency and improve overall production efficiency. The proposed motion-coordination scheme employs a distributed and scalable architecture, supporting coordination across heterogeneous multi-robot systems. Two optimization methodologies are introduced to manage kinematic redundancies and maintain continuous, near-optimal operation throughout the manufacturing process. The first strategy exploits a task-space dimensionality reduction to achieve locally optimal configurations by leveraging symmetry-axis rotations of the tool. The second strategy utilizes the workpiece positioner to drive the coordinated robots toward stable and kinematically favorable configurations. For both optimization strategies, multiple objectives are defined to improve key performance metrics, including manipulability, configuration consistency, proximity to mechanical limits, and motion efficiency. Addressing a key limitation of existing coordination approaches, the framework is designed around online setpoint modification, allowing coordinated robots to respond effectively to in-situ process feedback. The proposed control framework is validated using the Robot Operating System (ROS) middleware on a combination of physical and simulated multi-robot system hardware.

Arbogast, Alex [ORNL] (ORCID:0000000154740723)↗

NASA NTRS2001

Free-form Airfoil Shape Optimization Under Uncertainty Using Maximum Expected Value and Second-order Second-moment Strategies

Free-form shape optimization of airfoils poses unexpected difficulties. Practical experience has indicated that a deterministic optimization for discrete operating conditions can result in dramatically inferior performance when the actual operating conditions are different from the - somewhat arbitrary - design values used for the optimization. Extensions to multi-point optimization have proven unable to adequately remedy this problem of "localized optimization" near the sampled operating conditions. This paper presents an intrinsically statistical approach and demonstrates how the shortcomings of multi-point optimization with respect to "localized optimization" can be overcome. The practical examples also reveal how the relative likelihood of each of the operating conditions is automatically taken into consideration during the optimization process. This is a key advantage over the use of multipoint methods.

Huyse, Luc↗