Search NASA⌕ Search

SEARCH · Search NASA

Results for “performance monitoring”

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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Counterintuitive Compatibilization of Poly(-Valerolactone) and Poly(L-Lactic Acid) by Statistical Copolymers toward Compostable and Recyclable Packaging

Poly(δ-valerolactone) (PVL) and poly(l-lactic acid) (PLLA) are bioderivable, compostable, and chemically recyclable plastics with synergistic properties for addressing plastic waste accumulation in receiving environments. Though recyclable-by-design polymers often fall short of competing with incumbent materials, blending affords a means to leverage individual component strengths toward ideal tunable properties. Polymer blends are often immiscible, but a range of methodologies are available to promote mixing. Here, we report on the compatibilization of 9 immiscible PVL and PLLA blends with three different compatibilization agents: thermoplastic starch, synthesized PVL-co-PLLA statistical copolymers (SCPs), and synthesized PVL-b-PLLA block-type copolymers. Resulting degrees of compatibilization are observed through scanning electron microscopy, corroborated by thermal and mechanical analyses monitoring performance as a function of microdomain size. Small-angle and wide-angle X-ray scattering experiments are conducted to observe the influence of compatibilizers on individual crystalline phases to further elucidate material behavior. Molecular dynamics simulations provide key insights into the interfacial interactions between homopolymers and compatibilizers. Finally, a suite of end-of-life avenues is established by biodegradation in industrial composting conditions, chemical recycling by deconstruction to hydroxymethyl esters, and direct chemical depolymerization to lactone precursors in mixed feed. Overall, we highlight several promising blends and the counterintuitive SCP compatibilization phenomenon toward high-performance, sustainable materials.

Compatibilization, copolymer compatibilizer, bio-b↗
DOE OSTI2020

Interstate-24 MOTION: Closing the Loop on Smart Mobility

We introduce the I-24 Mobility Technology Interstate Observation Network (MOTION), a transportation cyber-physical systems testbed under development in Tennessee. It consists of a six-mile freeway segment instrumented with 400 4K resolution cameras, processed by a real-time compute system to enable continuous performance monitoring of freeway traffic. The testbed is being developed to support next generation connected and autonomous vehicle technologies and advanced traffic management. When complete, the testbed will be the longest continuously observed freeway segment in the world. This article introduces the testbed, discusses the core design choices, and outlines the preliminary work conducted to support the design.

Gloudemans, Derek↗
DOE OSTI2024

Data-driven Modeling for Grid Edge IBRs: A Digital Twin Perspective of User-Defined Models

Recent events in Odessa have brought attention to the challenges associated with the interaction between Inverter- Based Resources (IBRs) and the transmission and distribution system. The NERC event diagnosis report has highlighted sev- eral issues, emphasizing the need for continuous performance monitoring of these IBRs by system operators. Key areas of concern include the mismatch of control and protection perfor- mance of IBRs between the original equipment manufacturer (OEM)-provided models and field measurements. The inability to replicate the realistic response can result in incorrect reliability and resilience studies. In this paper, we developed an approach on how to emulate the behavior of an IBR using measurement data obtained for system operators to utilize in real-time and long- term planning. Two experiments are conducted in the phasor domain and electromagnetic transients (EMT) domain to emulate the behavior for grid forming and grid following inverters under various operating conditions and the effectiveness of the proposed model is demonstrated in terms of accuracy and ease of utilizing user-defined models (UDMs)

Mahapatra, Kaveri [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2024

Transmission Data-Driven User-Defined Model for Inverter-based and Conventional Power Plants

Recent events in Odessa [1], [2] have shed light on the complexities of integrating large Inverter-Based Resource (IBR) plants with the transmission system, prompting NERC to stress continuous performance monitoring by transmission operators. Challenges such as plant control updates, IBR model revisions, Phase-locked loop loss of synchronism, and protection events have been identified, underscoring the need for enhanced monitoring protocols by regulatory bodies. The recent FERC 901 order underscores the importance of accurate data exchange regarding IBRs for reliability studies. However, limited access to IBR plant-related data hampers effective decision-making for transmission operators (TOP). This paper proposes a method for constructing data-driven User-Defined dynamic Models (UDM) for power plants for validating multiple-event data using field measurements from interconnection bus locations. The problem is formulated as a power plant model identification problem and a multi-task learning approach under partial input observability assumptions is proposed in this work. This approach aims to predict aggregated responses of conventional and IBR power plants during various dynamic physical events which is useful for planning studies under diverse disturbance conditions. Ultimately, this methodology emphasizes the importance of plant visibility to operators in addressing power system challenges, facilitating improved planning and operational studies.

Mahapatra, Kaveri [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2020

Dynamic Undervolting to Improve Energy Efficiency on Multicore X86 CPUs

Chip manufacturers introduce redundancy at various levels of CPU design to guarantee correct operation, even for worst-case combinations of non-idealities in process variation and system operating conditions. This redundancy is implemented partly in the form of voltage margins. However, for a wide range of real-world execution scenarios these margins are excessive and merely translate to increased power consumption, hindering the effort towards higher-energy efficiency in both HPC and general purpose computing. Our study on the x86-64 Haswell and Skylake multicore microarchitectures reveals-wide voltage margins, which vary across different microarchitectures, different chip parts of the same microarchitecture, and across different workloads. We find that it is necessary to quantify-voltage margins using multi-threaded and multi-instance workloads, as characterization with single-threaded and single-instance workloads that do not stress the CPU to its full capacity typically identifies overly optimistic margins that lead to errors when applied in realistic program execution scenarios. In addition, we introduce, deploy and evaluate a run-time governor that dynamically reduces the supply voltage of modern multicore x86-64 CPUs. Our governor employs a model that takes as input a set of performance metrics which are directly measurable via performance monitoring counters and have high predictive value for the minimum tolerable supply voltage (V min ), to predict and apply the appropriate reduction for the workload at hand. Compared with the conventional DVFS governor, our approach in this study achieves up to 42 percent energy savings for the Skylake family and 34 percent for the Haswell family for complex, real-world applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

CAN-D: A Modular Four-Step Pipeline for Comprehensively Decoding Controller Area Network Data

Controller area networks (CANs) are a broadcast protocol for real-time communication of critical vehicle subsystems. Original equipment manufacturers of passenger vehicles hold secret their mappings of CAN data to vehicle signals, and these definitions vary according to make, model, and year. Without these mappings, the wealth of real-time vehicle information hidden in the CAN packets is uninterpretable, severely impeding vehicle-related research, including CAN cybersecurity and privacy studies, aftermarket tuning, efficiency and performance monitoring, and fault diagnosis to name a few. Guided by the four-part CAN signal definition, we present CAN-D (CAN-Decoder), a modular, four-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte ordering), signedness (bit-to-integer encoding), and by leveraging diagnostic standards, augmenting a subset of the extracted signals with meaningful, physical interpretation. En route to CAN-D, we provide a comprehensive review of the CAN signal reverse engineering research. All previous methods ignore endianness and signedness, rendering them incapable of decoding many standard CAN signal definitions. Incorporating endianness grows the search space from 128 to 4.72E21 signal tokenizations and introduces a web of changing dependencies. In response, we formulate, formally analyze, and provide an efficient solution to an optimization problem, allowing identification of the optimal set of signal boundaries and byte orderings. In addition, we provide two novel, state-of-the-art signal boundary classifiers—both of which are superior to previous approaches in precision and recall in three different test scenarios—and the first signedness classification algorithm, which exhibits a $>$ 97% F-score. Altogether, CAN-D is the only solution with the potential to extract any CAN signal that is also the state of the art. In evaluation on 10 vehicles of different makes, CAN-D's average $\ell ^1$ error is five times better (81% less) than all previous methods and exhibits lower average error, even when considering only signals that meet prior methods’ assumptions. Finally, CAN-D is implemented in lightweight hardware, allowing for an on-board diagnostic (OBD-II) plugin for real-time in-vehicle CAN decoding.

42 ENGINEERING↗
DOE OSTI2021

AUTOPERF

AutoPerf is a collection of modules for low-overhead performance monitoring on HPC systems, the modules collect monitoring data such as MPI data, network counter data etc. Autoperf modules interoperate with the Open source Darshan software (xgitlab.cels.anl.gov/darshan/) as submodules and leverages the Darshan's log recording and analysis framework.

CHUNDURI, DEVISUDHEER KUMAR↗
DOE OSTI2020

White Earth Community Facilities Solar PV Project (Final Report)

Funded project was for the design, installation, and performance monitoring of 2 - 11.4 kW AC and 1-38.0 kW AC ground-mount solar installations for three tribal community facilities, for a total 60.8 kW AC, including a daycare facility and two community centers. Of in excess of 120 tribal buildings these facilities had showed the best promise for implementing solar, based upon site characteristics, roof loads, roof orientation and building size. The White Earth Nation seeks to move forward proactively to address the following project goals and objectives: 1) Reduction of energy costs, 2) Increasing energy security for Indian Tribes and tribal members, 3) Adding 60.8 KW in renewable energy generation (solar PV), 4) Offsets, reduces coal-fired electric energy demand by 76,397 kWh, 817,448 Source Energy Btu annually, and S) Meets the overall solar PV target set in the FOA.

14 SOLAR ENERGY↗
DOE OSTI2022

High-Temperature Creep Test Suite for Grizzly

A recent development in the Grizzly has focused on improving its ability to model the high-temperature response of alloys, enabling its use for predictive modeling of material response for advanced reactor applications. This report documents an effort to expand the example and assessment test suites in Grizzly to include a set of problems that exercise the models for high-temperature response of components relevant to advanced reactors. These problems can be used to assess the accuracy of models, provide examples for users interested in modeling this class of problems, monitoring performance on large-scale problems and as test cases for future feature development.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Transportation Plan Los Alamos National Laboratory FY2023

Los Alamos National Laboratory’s (LANL’s) mission expansion is expected to increase the workforce population (badge holders) to more than 18,000 by 2028. Before the COVID-19 pandemic, traffic congestion was notable and was impeding commuting workers in the forms of traffic congestion, travel delay, inconvenience, frustration, and time lost hunting for parking near an office location or programmatic operation. The Laboratory transportation strategy is moving toward transit-based commuting and not status quo expansion of onsite parking and roadways to serve this growing workforce. This plan addresses a multi-faceted transit and transportation solution. The body of the plan describes each aspect of this solution and concludes with a recommended set of Fiscal Year 2023 (FY23) investments planned as next steps toward implementation. The broad strategy is to offer agility and program depth for services and systems that can enable LANL to confidently invest, monitor performance, seek participant feedback, and make necessary adjustments.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Maintenance Plan for the Composite Analysis of the Hanford Site, Southeast Washington

The objective of this document is to provide a maintenance plan (MP) that supports the Hanford Site composite analysis (CA). Maintenance consists of four activities: compliance and performance monitoring; research and development; planned reviews and analysis; and revisions to the CA. This MP will be reviewed annually by the site and updated as needed to address priorities based upon new information or proposed changes, the status of any disposal authorization statement (DAS) conditions/limitations and Low-Level Waste Disposal Facility Federal Review Group (LFRG) issues. This revision supersedes Revision 4.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

NFE-24-10417: Durability and Life-time Energy Performance Enhancement of Vapor Compression Systems

This project investigated the use of a Centrifugal Particle Separation (CPS) system to improve the durability and operational reliability of outdoor heat exchangers in vapor-compression based heating, ventilation, air conditioning, and refrigeration (HVACR) systems. The developed method introduced a novel dust and fouling mitigation approach by integrating an ambient air pre-cleaner that employs centrifugal forces to remove particulate matter from incoming air before it reaches the heat exchanger. By reducing the accumulation of dust and debris on coil surfaces, the CPS system aims to lower maintenance requirements and extend equipment lifespan. The project specifically involves coupling a centrifugal air pre-cleaner with a single-zone ductless mini-split system and evaluating its effectiveness in maintaining cleaner coil surfaces. Additionally, the study examined the impact of CPS integration on system energy performance. Through controlled experimental testing and performance monitoring, this work seeks to validate CPS-based air pre-cleaning as a practical, efficient solution for mitigating outdoor coil dust accumulation and fouling in both residential and commercial HVACR applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Digitally Optimized Autonomous Robotic System for Hanford Waste Tank Handling [Slides]

Idaho National Laboratory (INL) is developing a robotic system and digital twin to perform monitoring, inspection, and mapping of underground tank pits at the Hanford Site, operated by the U.S. Department of Energy (USDOE). The Autonomous Pit Exploration System (APES) will consist of four primary components: an electric vehicle truck with a robotic system developed by INL to deploy payloads into the tanks through small diameter preexisting risers, a robotic arm developed by Florida International University (FIU) for the purposes of deploying a variety of payloads into the tank pit environment, and a small robotic COTS crawler platform for examining the bottom of the pits.

42 - ENGINEERING↗
DOE OSTI2021

Revisiting “Additional Carbon”: Tracking Atmosphere–Ecosystem Carbon Exchange to Establish Mitigation and Negative Emissions From Bio-Based Systems

Climate stabilization plans rely heavily on advanced bioenergy and bioproducts for substitution of fossil-based energy sources and materials, and increasingly, for negative emissions via the direct sequestration of biogenic carbon. Yet, there remain persistent, largely unresolved critiques of bioenergy assessment methodology, particularly in the areas of land use and biogenic carbon accounting. The concept of “additional carbon” calls for evaluating the climate performance of bio-based systems by whether feedstock production creates measurable new local agro-ecosystem uptake of carbon from the atmosphere. This concept is challenging to operationalize for first-generation biofuels, and has largely been advanced as a negative critique. However, carbon additionality is more straightforward to establish—and less critical to overall system mitigation performance—in advanced bioenergy systems. In this Perspective, I review the additional carbon critique, and why it is analytically challenging to address in first-generation biofuel systems based on conventional food crops with large existing markets. Next, I make a case that carbon additionality (1) is more readily achievable with cellulosic feedstocks, (2) is more directly observable for dedicated biomass crops, and (3) is not a strict requirement for achieving net mitigation in carbon-negative bio-based systems. I end by discussing how centering atmosphere–ecosystem carbon exchanges in bio-based system assessment could create new opportunities for enterprise-scale performance monitoring and verification, augmenting and diversifying the current reliance on model-based life-cycle assessment approaches.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Network-Scale Ubiquitous Volume Estimation Using Tree-Based Ensemble Learning Methods

Currently ubiquitous volume data for roadway networks remains the key missing dimension in traffic operations. Most volume data are average annual daily traffic (AADT) measures derived from the Highway Performance Monitoring System (HPMS). Although methods to factor the AADT to hourly averages for typical day of week exist, actual volume data is limited to a sparse collection of locations in which volumes are continuously recorded. This paper/poster explores the use of state-of-art machine learning techniques to estimate accurate volume measures that span the highway network providing ubiquitous coverage in space, and point-in-time measures for a specific date and time. Three tree-based ensemble learning models, random forest (RF), gradient boost machine (GBM), and extreme gradient boost (XGBoost), were tested for volume estimation by learning from combined dataset of commercial probe data provided by TomTom, the FHWA's Travel Monitoring Analysis System (TMAS) data, and other infrastructure attributes such as number of lanes, speed limit, and weather. The methods were tested on major corridors and freeways in the metropolitan area of Denver. All three machine learning methods were able to provide hourly volume estimates 24 hours a day, 7 days a week, and 365 days a year with around 18% mean absolute error to true volume and about 5% of error with respect to roadway capacity. The low error measures allow the potential application by transportation agencies.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Hybrid Analytics Solution for Power Plant Operations

Expert Microsystems, Inc., MapEx Software Inc. and XMPLR Energy LLC have teamed to submit a proposal to the US Department of Energy’s Office of Fossil Energy to improve the efficiency, reliability and flexibility of existing coal-based power plants. The project, titled Hybrid Analytics Solution to Improve Coal Power Plant Operations (the “Hybrid Analytics” project), will develop, demonstrate and commercialize a novel approach that integrates two proven real-time monitoring techniques. The Hybrid Analytics approach integrates into a single, integrated solution: a) an established, advanced data-driven analytics solution that includes artificial intelligence, advanced pattern recognition and machine-learning techniques and b) a well-proven, first principle thermal heat balance model solution. The future role of coal plants will depend on the ability to cycle and follow loads to meet marginal power demands, accommodate renewables and support the grid. Essential to success in this new role is a plant’s ability to maintain high reliability, efficiency and flexibility. However, these changing operational demands put significant stresses on power plant components that directly impact the ability to deliver these requirements. In the past, plants have independently monitored performance using physics-based solutions (heat rate, boiler losses, etc.) and reliability using data-driven analytics (advanced pattern recognition). The Hybrid Analytics solution integrates both approaches to enhance fault detection, provide automated diagnostics and estimates of remaining time to act, and provide critical, enhanced and accurate real-time information to coal plant operators. As a result, coal plants can improve efficiency, reliability, and flexibility and can cost-effectively maintain their key role in the power delivery system.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Controller area network decoder (CAN-D)

A system and method for decoding an unknown automotive controller area network (“CAN”) message definitions. CAN data vehicle signal mappings are typically held in secret and varied by automotive model and year. Without knowledge of the mappings, the wealth of real-time vehicle data hidden in the automotive CAN packets is uninterpretable—impeding research, after-market tuning, efficiency and performance monitoring, fault diagnosis, and privacy-related technologies. This technology can ascertain the CAN signals' boundaries (start bit and length), endianness (byte ordering), signedness (binary-to-integer encoding) from raw CAN data. This allows conversion of CAN data to time series. Interpreting the translated CAN data's physical meaning and finding a linear mapping to standard units (e.g., knowing the signal is speed and scaling values to represent units of miles per hour) can be achieved for many signals by leveraging diagnostic standards to obtain real-time measurements of in-vehicle systems. The system and method can be integrated into lightweight hardware enabling an OBD-II plugin for real-time in-vehicle CAN decoding or run on standard computers. The system can output a standard DBC file with the signal definition information.

Verma, Kiren E.↗