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At least 127 records · Page 7

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Planning Satellite Swarm Measurements for Climate Models: Comparing Dynamic Constraint Processing and MILP Methods

We present D-SHIELD, a challenging climate science application to plan coordinated measurements (observations) for a constellation of satellites, each containing two different sensors, each with 61 pointing angle options. The L-band and P-band radar sensors collect data fed into a soil moisture model which tracks and predicts soil moisture across 1.67 million Ground Positions (GP). Soil moisture is an important predictor of wildfires, and then a predictor of floods, landslides and debris flow after a fire. Each measurement covers multiple GP due to the sensor footprint. Each GP has a "model error" which represents the uncertainty of the the soil moisture state prediction. Model error changes at different rates for each GP as the time since last observation increases and after significant events like rain. The planner's goal is to select measurements which maximize soil moisture model improvement (reduce model uncertainty). This problem is combinatorically explosive, involving many degrees of freedom for planner choices. Good domain heuristics can find solutions within a reasonable time for our application needs but cannot be proven optimal. In this paper we compare two different planning approaches to this problem: Dynamic Constraint Processing (DCP) and Mixed Integer Linear Programming (MILP). We match inputs and metrics for both DCP and MILP algorithms to enable a direct apples-to-apples comparison. We demonstrate and discuss the trades between DCP flexibility and performance vs. MILP's promise of provable optimality.

Rich Levinson↗

DOE OSTI2022

Li-Ion Battery Electrode Contact Resistance Estimation by Mechanical Peel Test

Li-ion battery electrode electronic properties, including bulk conductivity and contact resistance, are critical parameters affecting cell performance and fast-charge capability. Contact resistance between the coating and current collector is often the largest electronic resistance in an electrode and is affected by chemical, microstructural, and interfacial variations. Direct measurements of contact resistance and bulk conductivity have proven to be challenging. In their absence, a mechanical electrode peel test is often used to compare adhesion and electrical contact resistance. However, using a micro-flexible-surface probe, contact resistance can be directly determined. Here, this work compares contact resistance and mechanical peel strength of multiple commercial-grade HE5050 and NCM523 cathodes and graphite and silicon anodes. It was found that peel strength correlates well with contact resistance in a carefully curated data set (p < 0.05) and in some situations may be a good metric to estimate electrical properties. However, there were distinct outliers in the data set, indicating that peel strength may not accurately reflect electrical properties when there is significant variation in electrode composition. These results illustrate the value of the micro-flexible-surface probe in quantifying contact resistance and bulk conductivity to better understand how battery composition and processing steps affect microstructure and resulting cell performance.

25 ENERGY STORAGE↗

DOE OSTI2022

Material aspects of giga-Hertz ZnO TFTs for wireless systems

Abstract Having enabled high-value application capabilities through mass production of flat-panel displays, X-ray imagers, and solar panels, Large-Area Electronics (LAE) holds potential to open new frontiers in wireless applications for the Internet of Things and 5G/6G, by enabling unprecedented spatial control and power efficiency through large size and flexible form factor of radiative apertures. However, this requires boosting operation frequencies from the traditional limits in the range of 10–100’s of mega-Hertz to multi giga-Hertz. In this paper, we discuss critical device metrics, to characterize zinc-oxide (ZnO) thin-film transistor (TFT) operation frequency for both active (for signal amplification) and passive components in LAE-based circuits and systems. We then describe the key structural and material approaches towards recently demonstrated LAE-based giga-Hertz wireless systems employing ZnO TFTs. Bringing LAE to the giga-Hertz regime provides a path towards flexible and meter-scale monolithic integrated wireless systems. Graphical abstract

Ma, Yue (ORCID:0000000186724090)↗

DOE OSTI2020

Experiences with a Flexible User Research Process to Build Data Change Tools

Scientific software development processes are understood to be distinct from commercial software development practices due to uncertain and evolving states of scientific knowledge. Sustaining these software products is a recognized challenge, but under-examined is the usability and usefulness of such tools to their scientific end users. User research is a well-established set of techniques (e.g., interviews, mockups, usability tests) applied in commercial software projects to develop foundational, generative, and evaluative insights about products and the people who use them. Currently these approaches are not commonly applied and discussed in scientific software development work. The use of user research techniques in scientific environments can be challenging due to the nascent, fluid problem spaces of scientific work, varying scope of projects and their user communities, and funding/economic constraints on projects.In this paper, we reflect on our experiences undertaking a multi-method user research process in the Deduce project. The Deduce project is investigating data change to develop metrics, methods, and tools that will help scientists make decisions around data change. There is a lack of common terminology since the concept of systematically measuring and managing data change is under explored in scientific environments. To bridge this gap we conducted user research that focuses on user practices, needs, and motivations to help us design and develop metrics and tools for data change. This paper contributes reflections and the lessons we have learned from our experiences. We offer key takeaways for scientific software project teams to effectively and flexibly incorporate similar processes into their projects.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2005

Turbulence Hazard Metric Based on Peak Accelerations for Jetliner Passengers

Calculations are made of the approximate hazard due to peak normal accelerations of an airplane flying through a simulated vertical wind field associated with a convective frontal system. The calculations are based on a hazard metric developed from a systematic application of a generic math model to 1-cosine discrete gusts of various amplitudes and gust lengths. The math model simulates the three degree-of- freedom longitudinal rigid body motion to vertical gusts and includes (1) fuselage flexibility, (2) the lag in the downwash from the wing to the tail, (3) gradual lift effects, (4) a simplified autopilot, and (5) motion of an unrestrained passenger in the rear cabin. Airplane and passenger response contours are calculated for a matrix of gust amplitudes and gust lengths. The airplane response contours are used to develop an approximate hazard metric of peak normal accelerations as a function of gust amplitude and gust length. The hazard metric is then applied to a two-dimensional simulated vertical wind field of a convective frontal system. The variations of the hazard metric with gust length and airplane heading are demonstrated.

Stewart, Eric C.↗

DOE OSTI2022

Model-Based Framework to Optimize Charger Station Deployment for Battery Electric Vehicles

The development of battery electric vehicles (BEVs) is accelerating due to their environmental advantages over gasoline and diesel-powered vehicles, including a decrease in air pollution and an increase in energy efficiency. The deployment of charging infrastructure will need to increase to keep pace with demand, especially for large commercial vehicles for which few public chargers currently exist. In this paper, a new flexible framework is proposed for optimizing the placement of charging stations for BEVs, within which different physical models and optimization techniques may be used. Furthermore, a set of metrics is suggested to help enforce complex constraints and facilitate direct comparison between different optimization techniques. Unlike many existing charger placement techniques, the proposed method directly considers the historical driving patterns on a vehicle-by-vehicle basis, using transparent models to assess impacts of candidate charger placements, thus improving the explainability of the results. In the developed framework, modeled BEVs are first generated along the road network to mimic historical traffic data and are simulated traveling along a given route according to a simplified vehicle model. During the simulation, the charger placement problem is initially relaxed to allow vehicles to charge at any node along the road network, and vehicle states are tracked to assess areas of high charging demand. Charging stations are then placed based on the results of the relaxed simulation, and suggested placements are evaluated via road network simulation with fixed charger locations. This proposed framework is applied to a sample problem of placing charging stations along five major highway corridors for Class 8 over-the-road electric trucks. A novel mixed integer programming (MIP) formulation is proposed to optimize charger placements based upon the expected charging demand. Constraints were imposed on the final placement results to limit expected wait times at each station and ensure a minimum threshold of trucking routes are viable for BEVs. The results demonstrate the flexibility and potential effectiveness of the developed model-based framework for scalable charger station deployment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2003

Nuclear Electric Vehicle Optimization Toolset (NEVOT): Integrated System Design Using Genetic Algorithms

The Nuclear Electric Vehicle Optimization Toolset (NEVOT) optimizes the design of all major Nuclear Electric Propulsion (NEP) vehicle subsystems for a defined mission within constraints and optimization parameters chosen by a user. The tool uses a Genetic Algorithm (GA) search technique to combine subsystem designs and evaluate the fitness of the integrated design to fulfill a mission. The fitness of an individual is used within the GA to determine its probability of survival through successive generations in which the designs with low fitness are eliminated and replaced with combinations or mutations of designs with higher fitness. The program can find optimal solutions for different sets of fitness metrics without modification and can create and evaluate vehicle designs that might never be conceived of through traditional design techniques. It is anticipated that the flexible optimization methodology will expand present knowledge of the design trade-offs inherent in designing nuclear powered space vehicles and lead to improved NEP designs.

Tinker, Michael L.↗

NASA NTRS2011

Advanced Ablative TPS

Early NASA missions (Gemini, Apollo, Mars Viking) employed new ablative TPS that were tailored for the entry environment. After 40 years, heritage ablative TPS materials using Viking or Pathfinder era materials are at or near their performance limits and will be inadequate for future exploration missions. Significant advances in TPS materials technology are needed in order to enable any subsequent human exploration missions beyond Low Earth Orbit. This poster summarizes some recent progress at NASA in developing families of advanced rigid/conformable and flexible ablators that could potentially be used for thermal protection in planetary entry missions. In particular the effort focuses technologies required to land heavy (approx.40 metric ton) masses on Mars to facilitate future exploration plans.

Gasch, Matthew J.↗

DOE OSTI2024

From Simple Labels to Time-Use Integrations: Supporting the Spectrum of Qualitative Travel Behavior Data

In transportation research, applications of travel behavior data collection are context-specific and require different types of qualitative inputs. These inputs can be viewed as spanning a spectrum of user burden and data quality, from simple trip labels to complex time-use surveys. However, each currently active smartphone-based travel diary platform appears to only support one type of qualitative input, and the effort required for customization is unclear. In this paper, we characterize the spectrum by defining four canonical use cases: (i) trip labels, (ii) trip questionnaire, (iii) counterfactual trips, and (iv) time-use surveys. We then outline a mechanism for supporting configurable user inputs on the same underlying smartphone-based sensing mechanism and demonstrate that it can support all the use cases without any code changes. We further demonstrate that the flexible data model that underpins this mechanism can enable real-time monitoring and analysis. Finally, we evaluate per-user data collection and engagement metrics for large-scale deployments of three canonical use cases, spanning 10 programs, 435 users, and 251,041 trips, and a maximum duration of 800 days. Future efforts may support additional use cases through an expanded configuration and provide greater insight into user engagement. We hope that these insights enable the research community to look at qualitative inputs through a new lens and experiment with novel use cases to fill in the spectrum.

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗

DOE OSTI2022

Insights into Methodologies and Operational Details of Resource Adequacy Assessment: A Case Study with Application to a Broader Flexibility Framework

Assessing and maintaining resource adequacy (RA) is a core pillar of power systems. However, recent changes in the physical makeup of these systems and the conditions under which these systems must operate have yielded a renewed interest in the methods, metrics, and assumptions that underpin RA assessments. In this paper, we systematically explore a wide range of RA modeling dimensions, including: the objective function and level of operational detail in the underlying model formulation; the quantity (look-ahead) and quality (accuracy) of data that is available for making operational decisions within those models; and the physical configuration of solar photovoltaics (PV) with battery storage hybrid resources. We apply a set of probabilistic RA tools and production cost modeling tools to a realistic test system based loosely on a future Electric Reliability Council of Texas power system dominated by solar PV resources. Under the assumptions of our system and models, we find that multi-stage probabilistic assessments may provide a more robust evaluation of RA by capturing a wider range of operational and system interactions, but this comes at a computational cost of 1-2 orders of magnitude longer run time depending on the specific configuration. In addition, the information on thermal generator availability impacts RA performance by an order of magnitude more than solar resource forecasts, which is driven by the comparatively larger magnitude of thermal outages than solar forecast errors within our test system. Lastly, the flexibility provided by hybrid and other resources can help reduce system load-shedding event frequencies and enable the system to be more robust to inaccurate forecast information, and alternative hybrid inverter sizes can impact RA levels by 1-2 orders of magnitude. Our results point to the importance of a broader flexibility framework to describe the interaction between (1) flexibility "supply" from both physical resource capabilities and operational constraints considered in the modeling, and (2) flexibility "demand" from forecast errors, thermal generator outages, and other sources of uncertainty, as well as their RA impacts. Results are likely sensitive to the system buildout explored; future work could consider additional system configurations and conditions.

ENERGY PLANNING, POLICY, AND ECONOMY,POWER TRANSMI↗

DOE OSTI2024

Short-Term Load Forecasting Considering EV Charging Loads with Prediction Interval Evaluation

Short-term load forecasting plays a critical role in power system planning and operation. Along with the electrification of various loads, electricity demands are becoming increasingly hard to predict. Notably, the recent rise in electric vehicles (EVs) has further contributed to this unpredictability. To address this issue, this paper proposes a probabilistic load forecasting strategy utilizing Gaussian process regression, structured in a day-ahead manner. While many works focus on deterministic prediction, probabilistic forecasting offers additional insights into variability and uncertainty, enabling more flexible and reliable operation for power systems. To enhance the accuracy of the load forecasting model, the inputs include features related to EV charging habits as well as commonly used weather information. The load forecasting results are evaluated using various metrics, including conventional ones that assess the accuracy of point forecasts, as well as additional metrics that test the reliability of prediction intervals. The proposed load forecasting method is finally tested on real residential power consumption data and EV charging data sampled from real-world sources. The results prove that the new features can greatly improve the performance of the load forecasting method.

electrical vehicle↗

DOE OSTI2024

A Novel Framework to Quantify Power Grid Resilience

The quantification of an operating power grid’s resilience is highly significant today, given its criticality as an enabler of other infrastructures, complexity, and the threat it faces due to a wide range of detrimental events, from extreme climate to cyber attacks. Currently, there exist no standardized definitions and metrics for measuring the resilience of an operating grid. In this paper, we introduce a novel resilience quantification framework and demonstrate a method to measure the flexibility towards topological/structural changes due to potential failures in the power grid to assess operational resilience. We start with the state estimation data from a large utility and use the graph analysis methods and power flow simulation tools to compute the identified resilience parameters.

Yoginath, Srikanth↗

NASA NTRS2018

Creating Innovative Frameworks to Spur Cultural Change at NASA Armstrong Flight Research Center

Changing the culture of an organization is a monumental task that often takes years and has no set formula. Steps can be taken, however, to spur cultural change by creating spaces and infrastructure to serve as the initial driving force. An innovation space and a bicycle sharing (bike share) program were implemented at the National Aeronautics and Space Administration (NASA) Armstrong Flight Research Center (AFRC) (Edwards, California) with a vision toward connecting Center personnel, fostering collaboration and innovation, retaining newer employees, promoting flexibility, and improving the culture and workplace atmosphere. This paper discusses the steps taken, challenges faced, novel culture-change-focused design elements, lessons learned, acquired metrics, and how these initiated cultural change at AFRC. For both the innovation space and the bike share program, funding was negotiated and provided through the NASA Convergent Aeronautics Solutions (CAS) project, which was seeking to improve the innovation and collaboration capabilities at each of the four NASA aeronautics Centers. Key stakeholders across AFRC from upper management, facilities, safety, engineering, and procurement were identified early in the process and were consulted and included throughout execution to ensure that any encountered roadblocks could be easily navigated. Research was then conducted by attending conferences and visiting culture-changing organizations both inside and outside United States Government agencies. Distilling the research, identifying available space, and deciding on specific design elements for the space was conducted by a subset of individuals of diverse backgrounds to enable quick, effective decision-making. Decisions were made with the intent to increase usage and diversity of users of the space; care was taken to ensure a well-crafted atmosphere that would foster the desired culture change. The allocated physical space required major structural modifications, new furniture, and new capabilities that would bring people in. Decisions and desires underwent a rapid reiterative process in order to stay within budget and short deadlines, while holding firm to what was seen as fundamental elements of an innovation space. The framework for cultural change being established, the more difficult task began: incubating the desired culture. Intentional workday use of the innovation space was encouraged, and organized events coordinated in order to truly foster culture change. Such incubation supports the organic spread of culture change to all areas of AFRC. This framework was complemented and expanded by the implementation of the bike share program. Steps for implementation included bike selection based on lessons learned, creating bike stations and signage, implementing bike share rules, and building a volunteer maintenance infrastructure. A novel user-reporting feedback system at each bike station is a low-impact method of capturing usage metrics. Due to the nature of the work conducted at AFRC, the bike share program and feedback system were negotiated and vetted through various organizations including legal, safety, and operations. The innovation space and the bike share program together are an effective initial framework for innovation and collaboration. Culture change takes time, but the innovation space and the bike share program are already showing signs of making a positive impact on the AFRC workforce.

rooms↗

DOE OSTI2020

The Future of Zero Energy Buildings: Produce, Respond, Regenerate: Preprint

The zero energy buildings concept is more than 20 years old, and the paradigm shift from buildings as energy consumers to buildings as energy producers is underway. Buildings also consume land and material resources, however, with attendant environmental impacts. Another paradigm is emerging: a built environment that produces energy and is environmentally responsive and regenerative. This paper investigates an updated framework for thinking about zero energy buildings that includes discussions of prioritizing renewables; determining on-site versus off-site generation; exploring how and when buildings should use energy; and balancing renewables, storage, and energy efficiency. Buildings are typically connected to the utility grid and the utility grid develops largely in response to the built environment. If more buildings’ real time electricity use aligned with renewable generation, more renewables would be added to the grid. Ultimately, the goal for zero energy buildings will be to use 100% renewables, 100% of the time, matching loads with energy storage and renewable generation at each discrete timestep over a year. This target is beyond the current zero energy definitions, which focus on an annual balance of renewable supply and energy demand and use the grid to “store” excess production to make up for hours without sufficient on-site renewable generation. This paper expands this upgraded concept and outlines simple metrics to evaluate the alignment of renewable sources and storage with building loads. This process can provide insights on building design considerations, including the use of flexible loads and optimal resource management.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗

DOE OSTI2021

WOMBAT (Windfarm Operations and Maintenance cost-Benefit Analysis Tool) WISDEM® [SWR-21-68]

The windfarm operations and maintenance cost-benefit analysis tool (WOMBAT) is software to simulate the operations and maintenance phase of either onshore or offshore windfarms. WOMBAT is a medium-fidelity tool that is designed to model turbine, cable, and substation failures at the subassembly level, but is flexible enough that individual component or asset-level modeling can also be performed. WOMBAT enables users to model technological improvements, the use of a wide range of service equipment from drone repairs and remote resets to heavy lift vessels and crawler cranes, to analyze cost trends with varying trade-offs in conjunction with energy production, availability and a growing range of metrics. This library provides a tool to simulate the operation and maintenance phase (O&M) of distributed, land-based, and offshore windfarms using a discrete event simulation framework. WOMBAT is written around the SimPy discrete event simulation framework. Additionally, this is supported using a flexible and modular object-oriented code base, which enables the modeling of arbitrarily large (or small) windfarms with as many or as few failure and maintenance tasks that can be encoded. Please note that this is still heavily under development, so you may find some functionality to be incomplete at the current moment, but rest assured the functionality is expanding. With that said, it would be greatly appreciated for issues or PRs to be submitted for any improvements at all, from fixing typos (guaranteed to be a few) to features to testing. Also available at: https://pypi.org/project/wombat/

Cooperman, Aubryn↗

DOE OSTI2022

Optimization-Based Resiliency Verification in Microgrids via Maximal Adversarial Set Characterization

Critical energy infrastructures are increasingly relying on advanced sensing and control technologies for efficient and optimal utilization of flexible energy resources. Algorithmic procedures are needed to ensure that such systems are designed to be resilient to a wide range of cyber-physical adversarial events. This paper provides a robust optimization framework to quantify the largest adversarial perturbation that a system can accommodate without violating pre-specified resiliency metrics. We formulate the maximal adversarial set characterization as a bi-level optimization problem which is solved via Lagrangian relaxations.We illustrate the proposed algorithm on an islanded microgrid example: a modified IEEE 123-node feeder with distributed energy resources. Simulations are carried out to characterize the tolerable adversarial perturbations for varying levels of available flexibility (energy reserves).

Nazir, Mohammad Nawaf↗

DOE OSTI2023

LOCOMOTIVES - Comprehensive Impact and Cost Assessment Framework of Carbon Lowering Approaches for the US Rail Freight System

The goal of this project is to develop a tool to aid railroads and other stakeholders assess and approach the decarbonization of freight rail operations by identifying new, viable low-carbon energy storage and conversion systems for future locomotive systems and how they should be deployed on the existing US freight rail network. In the first quarter, the project focused on collecting data, establishing a simulation workflow, and engaging industry through the creation of the Industry Advisory Board (IAB). In the second quarter, the project focused on selecting fuel pathways and powertrain technologies, setting performance targets, conducting a techno-economic analyses, and developing the simulation framework that would serve as the backbone of the future toolhead. The third quarter involved developing an industry-oriented interactive dashboard powered by a five-step sequential framework, as well as holding industry advisory board meetings as per the initial technology-to-market plan. In the remaining project quarters, the NUFRIEND dashboard were fine-tuned with the help of IAB member feedback and in-depth scenario analyses were conducted to support the techno-economic analysis of energy sources. Additionally, dashboard documentation, project insights, and open-source code on GitHub were prepared and released. Throughout the project, the team completed testing and analysis of all model components, integrated all initial test scenarios, and conducted stakeholder engagement. Lower-carbon drop-in fuels can be deployed as admixtures and are considered uniform across the network at a desired penetration rate, while hydrogen and battery-electric technology deployment poses a more complex problem as they require significant investments to be made in the siting of refueling/charging facilities and the replacement of locomotive fleets. Thus, strategies for locating and sizing refueling/charging facilities on a railroad’s network to meet their energy demands were developed to inform deployment decisions. To address this challenge, the Northwestern University Freight Rail Infrastructure & Energy Network Decarbonization (NUFRIEND) framework presents a five-step sequential framework to select O-D paths, locate facilities, reroute flows, size facilities, and evaluate the deployment for alternative energy sources that require locomotive powertrains to be converted and new refueling infrastructure to be deployed. The NUFRIEND Framework is an industry-oriented tool for simulating the deployment of new energy technologies across the US freight rail network. The framework provides a comprehensive network-level optimization and scenario simulation tool for decarbonizing the freight rail sector, addressing the uncertainties surrounding technological developments by supporting sensitivity analyses for different operational and technological parameters through a transparent and flexible input module. It offers practical alternatives to diesel locomotives and can be applied for any railroad considering the specific network structure and freight demand, outputting evaluation metrics for the associated emissions and costs relative to diesel operations. A number of relevant simulation scenarios were run and analyzed for key insights on the value of different alternative technologies for freight rail decarbonization. The project developments and findings have been presented at numerous conferences and events.

08 HYDROGEN↗

NASA NTRS1990

Dynamics and control of multipayload platforms - The Middeck Active Control Experiment (MACE)

A flight experiment entitled the Middeck Active Control Experiment (MACE) proposed by the Space Engineering Research Center (SERC) at the Massachusetts Institute of Technology is described. The objective of this program is to investigate and validate the modeling of the dynamics of an actively controlled flexible, articulating, multibody platform free floating in zero gravity. A rationale and experimental approach for the program are presented. The rationale shows that on-orbit testing, coupled with ground testing and a strong analytical program, is necessary in order to fully understand both how flexibility of the platform affects the pointing problem, as well as how gravity perturbs this structural flexibility causing deviations between 1-and 0-gravity behavior. The experimental approach captures the essential physics of multibody platforms, by identifying the appropriate attributes, tests, and performance metrics of the test article, and defines the tests required to successfully validate the analytical framework.

Miller, David W.↗