Results for “integrated demand management” — Page 15

NASA NTRS2002

Overview of the National Aeronautics and Space Administration's Nondestructive Evaluation (NDE) Program

NASA's Office of Safety and Mission Assurance sponsors an Agency-wide NDE Program that supports Aeronautics and Space Transportation Technology, Human Exploration and Development of Space, Earth Science, and Space Science Enterprises. For each of these Enterprises, safety is the number one priority. Development of the next generation aero-space launch and transportation vehicles, satellites, and deep space probes have highlighted the enabling role that NDE plays in these advanced technology systems. Specific areas of advanced component development, component integrity, and structural heath management are critically supported by NDE technologies. The simultaneous goals of assuring safety, maintaining overall operational efficiency, and developing and utilizing revolutionary technologies to expand human activity and space-based commerce in the frontiers of air and space places increasing demands on the Agencies NDE infrastructure and resources. In this presentation, an overview of NASA's NDE Program will be presented, that includes a background and status of current Enterprise NDE issues, and the NDE investment areas being developed to meet Enterprise safety and mission assurance needs through the year 2009 and beyond.

Generazio, Edward R.↗

NASA NTRS2008

Galileo - The Serial-Production AIT Challenge

The Galileo Project is one of the most demanding projects of ESA, being Europe's autarkic navigation system and a constellation composed of 30 satellites. This presentation points out the different phases of the project up to the full operational capability and the corresponding launch options with respect to launch vehicles as well as launch configurations. One of the biggest challenges is to set up a small serial 'production line' for the overall integration and test campaign of satellites. This production line demands an optimization of all relevant tasks, taking into account also backup and recovery actions. A comprehensive AIT concept is required, reflecting a tightly merged facility layout and work flow design. In addition a common data management system is needed to handle all spacecraft related documentation and to have a direct input-out flow for all activities, phases and positions at the same time. Process optimization is a well known field of engineering in all small high tech production lines, nevertheless serial production of satellites are still not the daily task in space business and therefore new concepts have to be put in place. Therefore, and in order to meet the satellites overall system optimization, a thorough interface between unit/subsystem manufacturing and satellite AIT must be realized to ensure a smooth flow and to avoid any process interruption, which would directly lead to a schedule impact.

Ragnit, Ulrike↗

NASA NTRS2012

Water Information System Platforms Addressing Critical Societal Needs in the Mena Region

The MENA region includes 18 countries, the occupied Palestinian territories and Western Sahara. However, the region of interest for this study has a strategic interest in countries adjacent to the Mediterranean Sea, which includes, Morocco, Tunisia, Egypt, Lebanon and Jordan. The 90% of the water in the MENA region is used for the agriculture use. By the end of this century. this region is projected to experience an increase of 3 C to 5 C in mean temperatures and a 20% decline in precipitation (lPCC, 2007). Due to lower precipitation, water run-off is projected to drop by 20% to 30% in most of MENA by 2050 Reduced stream flow and groundwater recharge might lead to a reduction in water supply of 10% or greater by 2050. Therefore, per IPCC projections in temperature rise and precipitation decline in the region, the scarcity of water will become more acute with population growth, and rising demand of food in the region. Additionally, the trans boundary water issues will continue to plague the region in terms of sharing data for better management of water resources. Such pressing issues have brought The World Bank, USAID and NASA to jointly collaborate for establishing integrated, modern, up to date NASA developed capabilities for countries in the MENA region for addressing water resource issues and adapting to climate change impacts for improved decision making and societal benefit. This initiative was launched in October 2011 and is schedule to be completed by the end of2015.

Habib, Shahid↗

DOE OSTI2024

Battery Energy Storage Systems: A Review of Energy Management Systems and Health Metrics

With increasing concerns about climate change, there is a transition from high-carbon-emitting fuels to green energy resources in various applications including household, commercial, transportation, and electric grid applications. Even though renewable energy resources are receiving traction for being carbon-neutral, their availability is intermittent. To address this issue to achieve extensive application, the integration of energy storage systems in conjunction with these resources is becoming a recommended practice. Additionally, in the transportation sector, the increased demand for EVs requires the development of energy storage systems that can deliver energy for rigorous driving cycles, with lithium-ion-based batteries emerging as the superior choice for energy storage due to their high power and energy densities, length of their life cycle, low self-discharge rates, and reasonable cost. As a result, battery energy storage systems (BESSs) are becoming a primary energy storage system. The high-performance demand on these BESS can have severe negative effects on their internal operations such as heating and catching on fire when operating in overcharge or undercharge states. Reduced efficiency and poor charge storage result in the battery operating at higher temperatures. To mitigate early battery degradation, battery management systems (BMSs) have been devised to enhance battery life and ensure normal operation under safe operating conditions. Some BMSs are capable of determining precise state estimations to ensure safe battery operation and reduce hazards. Precise estimation of battery health is computed by evaluating several metrics and is a central factor in effective battery management systems. In this scenario, the accurate estimation of the health indicators (HIs) of the battery becomes even more important within the framework of a BMS. This paper provides a comprehensive review and discussion of battery management systems and different health indicators for BESSs, with suitable classification based on key characteristics.

Nazaralizadeh, Solmaz (ORCID:0009000737004039)↗

DOE OSTI2025

Real-Time Lifetime Prediction of Semiconductor Devices Using Hardware-in-the-Loop

This paper presents a unique approach to enable real-time lifespan prediction of semiconductor power modules using a Hardware-in-the-Loop (HIL) system. By integrating the module's overall loss characteristics-specifically switching and conduction losses-with a thermoelectric model of the thermal management system, this research demonstrates that the model can dynamically estimates the junction temperature profile of the semiconductor devices in response to a changing torque demand profile for the motor drive system. This capability enables continuous monitoring of the module's operational time and cumulative stress induced on the devices to compute accumulated remaining lifetime or time-to-failure (TTF). This study provides an architectural framework for the HIL system with high-fidelity component models of multiple physical domains, allowing simulation of dynamic behaviors of a closely-coupled motor drive system. The advanced real-time computation and measurement functionalities of the HIL system allow for both dynamic lifetime calculations based on simulated data and aggregate lifetime predictions utilizing historical data. Moreover, this paper details an algorithm that not only computes cumulative damage but also synthesizes these data into a comprehensive aggregated lifetime metric. This methodology can enhance the maintenance scheduling strategies and operational reliability of semiconductor devices in critical applications, ultimately extending their service life while optimizing performance.

hardware-in-the-loop (HIL)↗

DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for System Engineering in the Energy Sector

Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world’s energy demands and build energy security.

42 - ENGINEERING↗

DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for Systems Engineering in the Energy Sector

Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world?s energy demands and build energy security.

42 - ENGINEERING↗

DOE OSTI2025

Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management

The urban drainage system constantly facing flooding issues in coastal and urban areas. Robust and accurate urban flood management, particularly considering fast-moving compound floods, is crucial to minimize the impact of flood disasters in coastal cities. Till now, Ho Chi Minh City (HCMC) lacks an effective means of urban flood management because of flood risk communication among residents. Existing flood risk communication tools rely on post-disaster flood model outcomes and data. Therefore, this research proposes a real-time Early Urban Flooding Warning System (EUFWS) integrated with a user-friendly web and app interface. The backbone of this system consists of flood models developed using machine learning (ML) algorithms, combined with big data and Web-GIS visualization, with ML serving as the core for constructing the EUFWS. EUFWS offer several key advantages: they are available at all times, accessible from anywhere, and provide a real-time, multi-user working platform. Additionally, the system is flexible, allowing for the easy addition of components and services and scalable, adjusting to workload demands. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. Research results indicate that EUFWS supported decision-makers to be effectively risk informed and make intelligent decisions during urban flood emergencies. Finally, this underscores the significant potential of integrating ML and information technology to enhance the management of smart urban drainage systems in flood-prone cities worldwide.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2021

Analysis of Impacts of Terminal Restrictions on Departures in D10 TRACON

The D10 – Dallas-Fort Worth Terminal Radar Approach CONtrol (TRACON) is an air traffic facility that controls and manages aircraft and airspace that consists of multiple airports located in the North Texas area. From an air traffic management standpoint, one of the main challenges in the D10 TRACON is the departure fix capacity as multiple airports compete for resources. This problem is magnified when en route demand/capacity imbalance and inclement weather around the TRACON reduce the capacity at the terminal fixes. This leads to multiple, dynamic Traffic Management Initiative (TMI) restrictions being issued by the Air Traffic Control (ATC) on departing flights. This, in turn, propagates delay to the surface of each airport within the metroplex. The NASA Airspace Technology Demonstration 2 (ATD-2) Phase 3 is deployed in D10 to demonstrate new technologies developed to manage the Integrated Arrival, Departure, and Surface (IADS) traffic in a metroplex environment where multiple airports are interacting and sharing resources at the terminal boundary. This paper uses the ATD-2 terminal restriction data collected in the D10 TRACON to quantify the impact of restrictions on the demand, analyze the relation between terminal restrictions and departure taxi time on airport surface, and establish relationships between restrictions and surface delay. We found that the restrictions on departure flights have a direct adverse effect on departure excess taxi time on the airport surface.

ATD-2↗

NASA NTRS2021

Analysis of Impacts of Terminal Restrictions on Departures in D10 TRACON

The D10 – Dallas-Fort Worth Terminal Radar Approach CONtrol (TRACON) is an air traffic facility that controls and manages aircraft and airspace that consists of multiple airports located in the North Texas area. From an air traffic management standpoint, one of the main challenges in the D10 TRACON is the departure fix capacity as multiple airports compete for resources. This problem is magnified when en route demand/capacity imbalance and inclement weather around the TRACON reduce the capacity at the terminal fixes. This leads to multiple, dynamic Traffic Management Initiative (TMI) restrictions being issued by the Air Traffic Control (ATC) on departing flights. This, in turn, propagates delay to the surface of each airport within the metroplex. The NASA Airspace Technology Demonstration 2 (ATD-2) Phase 3 is deployed in D10 to demonstrate new technologies developed to manage the Integrated Arrival, Departure, and Surface (IADS) traffic in a metroplex environment where multiple airports are interacting and sharing resources at the terminal boundary. This paper uses the ATD-2 terminal restriction data collected in the D10 TRACON to quantify the impact of restrictions on the demand, analyze the relation between terminal restrictions and departure taxi time on airport surface, and establish relationships between restrictions and surface delay. We found that the restrictions on departure flights have a direct adverse effect on departure excess taxi time on the airport surface.

ATD-2↗

DOE OSTI2024

A Multi-Dimensional Benefit Assessment of Automated Mobility Platforms (AMP) for Large Facilities

Automated Mobility Platform (AMP) is a general term for intelligently managed passenger mobility systems that leverage automation and electrification to improve system performance and sustainability through the integration of real-time data streams with advanced decision-making processes tailored for use in large facilities. Using airports as an example, the rapid growth in air travel demand has led to facility expansions and congested terminals, which directly impacts equity (e.g., increased challenges for passengers with reduced mobility [PRMs]) and sustainability - both of which are important metrics often neglected during the engineering design process. Therefore, to evaluate systems and inform critical decisions more effectively, a holistic evaluation framework is proposed that includes equity, sustainability, and infrastructure perspectives - referred to as a multidimensional benefit assessment. More specifically, the following analysis focuses on: (1) mobility, with emphasis on travel time and accessibility within an airport, contrasting existing systems with the proposed AMP system; (2) equity analysis, specifically to the PRM community, but with an eye to the whole of society; (3) energy consumption and greenhouse gas (GHG) emissions associated with intra-airport mobility, comparing and contrasting modes as well as impacts on overall airport operations efficiency; and (4) fundamental changes in building design enabled by AMPs for larger and more flexible, functional, and energy-efficient structures.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Accelerating science: The usage of commercial clouds in ATLAS Distributed Computing

The ATLAS experiment at CERN is one of the largest scientific machines built to date and will have ever growing computing needs as the Large Hadron Collider collects an increasingly larger volume of data over the next 20 years. ATLAS is conducting R&D projects on Amazon Web Services and Google Cloud as complementary resources for distributed computing, focusing on some of the key features of commercial clouds: lightweight operation, elasticity and availability of multiple chip architectures. The proof of concept phases have concluded with the cloud-native, vendoragnostic integration with the experiment’s data and workload management frameworks. Google Cloud has been used to evaluate elastic batch computing, ramping up ephemeral clusters of up to O(100k) cores to process tasks requiring quick turnaround. Amazon Web Services has been exploited for the successful physics validation of the Athena simulation software on ARM processors. We have also set up an interactive facility for physics analysis allowing endusers to spin up private, on-demand clusters for parallel computing with up to 4 000 cores, or run GPU enabled notebooks and jobs for machine learning applications. The success of the proof of concept phases has led to the extension of the Google Cloud project, where ATLAS will study the total cost of ownership of a production cloud site during 15 months with 10k cores on average, fully integrated with distributed grid computing resources and continue the R&D projects.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Advances in Desiccant Wheels for Dehumidification, VOC Mitigation, and CO 2 Removal for Energy-Efficient IAQ Management

Humidity control is pivotal to maintain occupant thermal comfort and suppress mold growth in indoor environments. Furthermore, poor indoor air quality (IAQ) due to the presence of volatile organic compounds (VOCs) and high concentrations (>1,000 ppm) of CO 2 can cause health issues and negatively affect cognitive performance. Therefore, providing high-quality indoor air has gained significant attention over the past decade. Conventional cooling coil and filter-based HVAC systems have limited capability to meet the augmented demand for occupant thermal comfort and high indoor air quality. Moreover, modern buildings are increasingly airtight to save energy, and increasing ventilation to mitigate VOC and CO 2 concentration is discouraged. Separate sensible and latent cooling technology using a rotary desiccant wheel presents a promising solution in this respect. Because of the development of desiccant materials with high water vapor, VOC, and CO 2 uptake, desiccant wheels can be used as an integrated technology option for IAQ management. Here, to promote desiccant wheel use for energy-efficient management of IAQ in buildings, this article reviews recent advancements in using desiccant wheels for dehumidification, VOC mitigation, and CO 2 capture from outdoor air. Finally, the article presents the authors’ perspective by summarizing the key research gaps in the field and discussing the future direction of research to address these gaps from two different aspects, namely, suitable adsorbent material development and desiccant wheel design.

CO2 capture↗

NASA NTRSDate not supplied

Autonomous Contingency Management In Urban Air Mobility: The Communication Network Awareness Machine System

Next Generation Air Transportation System (NextGen) has begun the modernization of the nation’s air transportation system (NAS), with goals to improve system safety, increase operation efficiency and capacity, provide enhanced predictability, resilience and robustness [1]. The overall objective of the Air Traffic Management-eXploration (ATM-X) project is to facilitate the goals of NextGen by conducting research to enable the growing demand of new, mission variant, air vehicles with safe access to the NAS. The implementation and utilization of new and burgeoning technologies that are both flexible, scalable, and systematically user-focused are requisite for ATM-X to achieve its intention of NAS safe entry [2]. Researchers from NASA Langley’s Flight Deck Integration Team have developed a system architecture that would allow ATM-X to leverage the necessary capabilities of an Increasingly Autonomous System (IAS), machine-agent that will promote the safe access and operation of air vehicles within what has become the byproduct of NextGen modernization, a Net-Centric airspace architecture and an Urban Air Mobility (UAM) community. Conducting flight operations within this type of architecture constrains the human-agent’s natural ability by data management. When the massive volume of data, its types, and the acquisition speed at which the data is ingested is observed it becomes evident that the human-agent will be functioning at an operational disadvantage. Therefore, the development and integration of intelligent machine-agents into the flight deck are a necessary implementation to achieve ATM-X overall objective of safe access and operation in the NAS.

Urban Air Mobility↗

DOE OSTI2026

A dual membrane-adsorption evaporator for solar-powered lithium extraction from complex brines

A reliable supply of lithium is required to meet the increased demand for batteries over the coming decades. In this work, we demonstrated the potential to effectively extract lithium from brines by coupling solar-powered evaporation, adsorption, and membrane technologies together. We first synthesized a three-dimensional adsorptive evaporator by coating a lithium manganese oxide material onto a cotton stick using an easily scalable, one-step method. An osmotic membrane was then installed at the root of the evaporator to enhance the lithium to magnesium selectivity, prevent scaling caused by divalent cations, and thus further increase the water evaporation flux. The operation of the dual membrane-adsorption evaporator is entirely driven by osmosis and capillary force, demanding no extra energy input. The integration of the osmotic membrane was found to increase the lithium to magnesium selectivity over 10-fold to higher than 40. The dual process also produced high lithium to calcium selectivity and proved to sustain long-term stability by self-managing its internal osmotic pressure. Last, a test was done in three simulated brine solutions with varied brine chemistry and the evaporation rate stayed high and consistent throughout the long-term test. This illustrates that this technology can be used in varied brine solutions throughout the world providing efficient lithium capture and separation from competing divalent cations.

Eskafi, Aydin F. [University of California, Berkel↗

DOE OSTI2025

Evaluating Thermostats' Deadbands Using HVAC Hardware-In-the-Loop Experiment for Advanced Control Strategies

Smart thermostats have gained significant popularity due to their potential for optimizing energy consumption and enhanced user control while ensuring occupants' comfort. The deadband, also referred to as temperature differential, is defined as the temperature difference between the desired setpoint and upper threshold or lower threshold for the HVAC equipment to turn on. It is a key factor influencing energy efficiency and user satisfaction. This paper presents a comparative analysis of the deadbands of five different smart thermostats, tested with a heat pump, aiming to identify variations in their deadband settings and implications for energy management. The experimental study was conducted using a HVAC hardware-in-theloop (HIL) system that integrates smart thermostats with physical HVAC equipment in a simulated house environment. The study explores the trade-offs between energy efficiency and occupant comfort and highlights how different thermostats participating in demand response event cycle differently based on their deadband settings. The findings offer valuable insights into how selecting the right thermostat or configuring smart thermostat with appropriate deadband settings can be leveraged to enhance demand response capabilities, shift loads effectively and improve operational flexibility in HVAC systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Evaluating Thermostats' Deadbands Using HVAC Hardware-In-the-Loop Experiment for Advanced Control Strategies: Preprint

Smart thermostats have gained significant popularity due to their potential for optimizing energy consumption and enhanced user control while ensuring occupants' comfort. The deadband, also referred to as temperature differential, is defined as the temperature difference between the desired setpoint and upper threshold or lower threshold for the HVAC equipment to turn on. It is a key factor influencing energy efficiency and user satisfaction. This paper presents a comparative analysis of the deadbands of five different smart thermostats, tested with a heat pump, aiming to identify variations in their deadband settings and implications for energy management. The experimental study was conducted using a HVAC hardware-in-theloop (HIL) system that integrates smart thermostats with physical HVAC equipment in a simulated house environment. The study explores the trade-offs between energy efficiency and occupant comfort and highlights how different thermostats participating in demand response event cycle differently based on their deadband settings. The findings offer valuable insights into how selecting the right thermostat or configuring smart thermostat with appropriate deadband settings can be leveraged to enhance demand response capabilities, shift loads effectively and improve operational flexibility in HVAC systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRSDate not supplied

Mapping National Forest Aboveground Biomass in Mexico By Integrating GEDI and Landsat Times Series Data

Mexico is one of the countries with great potential for the UN's Reducing Emissions from Deforestation and Forest Degradation (REDD+) program, a key nature-based solution for the forest sector. To monitor carbon stock changes, there is a growing demand for unbiased Monitoring Reporting Verification (MRV) systems to facilitate effective forest management and climate change mitigation strategies. Remote sensing-based national aboveground biomass density (AGBD) estimation over Mexico is scarce and often limited to one-time static mapping, leading to spatiotemporal inconsistency in inputs. As an effort under NASA's Carbon Monitoring System (CMS) program, we have developed a remote sensing-based approach to create consistent historical AGBD maps of Mexico using multi-stream remote sensing data, including spaceborne lidar GEDI and long-term Landsat time series, as well as topographic information. We employ the continuous change detection and classification (CCDC) algorithm for temporal modeling of Landsat surface reflectance, followed by the inference of forest AGBD using a random forest machine learning algorithm with the temporal information of land surface dynamics extracted by the CCDC as input. GEDI provides unprecedented forest structure and AGBD sampling datasets for model training and validation practices. In this presentation, we share the progress made in developing a spatially explicit mapping of historical AGBD changes associated with land surface changes and post-disturbance landscapes.

Taejin Park↗