Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Demand Response”

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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Solar Photovoltaics: Supply Chain Deep Dive Assessment

This is one of a series of reports and deep dive assessments produced in response to Executive Order 14017 “America’s Supply Chains,” which directs the Secretary of Energy to submit a report on supply chains for the energy sector industrial base. The Executive Order is helping the federal government to build more secure and diverse U.S. supply chains, including energy supply chains. Over the past decade, solar power has gone from an emerging, niche technology to a mature energy industry. By 2035, solar power could supply 40% or more of U.S. electricity demand, dramatically accelerating the decarbonization of buildings, transportation, and industry; and, if current technology trends continue, it could do so without increasing the price of electricity. The rapid expansion of solar energy has the potential to yield broad benefits in the form of economic activity and workforce development. The solar industry already employs roughly 230,000 people in the United States, at an average wage that is higher than the national average for most comparable positions. By decarbonizing the electricity sector by 2035, the U.S. solar industry could employ 500,000–1,500,000 people by 2030. The components that are assembled to install a photovoltaic power system are produced by a global supply chain. Photovoltaic (PV) modules (also called panels) are made of cells that use a variety of technologies. There are two leading types of solar modules used in the United States, with crystalline silicon (c-Si) modules representing 84% of the market and cadmium telluride (CdTe) modules representing 16% of the market. Modules of either type require mounting structures to provide mechanical support (racking), which may be configured to follow the sun (tracking). The output of any PV module is direct current (dc), which is almost always converted to alternating current (ac) by an inverter.

14 SOLAR ENERGY↗
DOE OSTI2024

TCF - Sustainable Well Cement for Geothermal, Thermal Recovery, and Carbon Storage wells

Primary cementing is the most important operation performed on a subterranean well. Cement, placed in the annulus between the casing and the formations, serves as a hydraulic seal preventing fluids and gas migrations, protecting steel casing from corrosion, and supporting the well structure. Poor cementing jobs can be directly responsible for wells not reaching their full capacity, casing corrosion, compromised well integrity and, in the worst-case scenarios, well collapse. Geothermal, thermal recovery, and carbon storage wells offer environments that are especially difficult for cements to survive while the required lifespan of these wells can be years. In these wells currently used cements cannot provide durable well integrity, and new solutions are necessary. The necessity of stabilizing the electric grid, increasing its flexibility, and providing energy on demand will further expand the market of durable cement solutions for applications in high-temperature underground reservoir thermal energy storage wells (HT RTES). The design challenges of special cement systems for such wells originate from chemistry limitations of currently used well cements, aggressive environments, temperature and repeated shock conditions associated with them, and very weak formations that are not uncommon in these wells. During the last 70 years the most common systems for thermal-well construction have been Ordinary Portland Cement (OPC) (absolute majority of the wells), silica-lime system, and high aluminum cement-containing formulations. The major issues of calcium-silicate hydrates, that form during the hydration of OPC resulting in hardened material, are their poor chemical resistance to acids due to the calcium interactions with acid anions followed by its eventual dissolution, even in mild acids, such as carbonic acid from CO 2 dissolution, and inadequate bonding to the steel causing serious casing corrosion problems (Sugama & Pyatina, 2019). Performance of OPC-based cement may be significantly improved with organic additives; however, those are limited by their temperature stability. An alternative to calcium-silicate cement was developed by BNL and commercialized by Halliburton as ThermaLock TM cement. Calcium-aluminate-phosphate-based chemistry of the material allowed overcoming acid-resistance problems of OPC, especially in CO 2 -rich geothermal environments. In 2012-2015, building upon this experience, BNL developed Thermal Shock Resistant Cement (TSRC) with the support of Geothermal Technology Office (GTO) of DOE. TSRC has high-temperature stable chemistry, superior properties of cement-metal casing bond and corrosion protection (4-times better than currently used OPC-based formulation), significantly outperforms common well-cements in thermal-shock tests, has self-healing properties, and like ThermaLock TM is CO 2 resistant incorporating carbonate ions into stable hydrates (Gill et al., 2012).

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Dynamic response of photorespiration in fluctuating light environments

Abstract Photorespiration is a dynamic process that is intimately linked to photosynthetic carbon assimilation. There is a growing interest in understanding carbon assimilation during dynamic conditions, but the role of photorespiration under such conditions is unclear. In this review, we discuss recent work relevant to the function of photorespiration under dynamic conditions, with a special focus on light transients. This work reveals that photorespiration is a fundamental component of the light induction of assimilation where variable diffusive processes limit CO2 exchange with the atmosphere. Additionally, metabolic interactions between photorespiration and the C3 cycle may help balance fluxes under dynamic light conditions. We further discuss how the energy demands of photorespiration present special challenges to energy balancing during dynamic conditions. We finish the review with an overview of why regulation of photorespiration may be important under dynamic conditions to maintain appropriate fluxes through metabolic pathways related to photorespiration such as nitrogen and one-carbon metabolism.

Plant Sciences↗
DOE OSTI2020

DAQExpert the service to increase CMS data-taking efficiency

The Data Acquisition (DAQ) system of the Compact Muon Solenoid (CMS) experiment at the LHC is a complex system responsible for the data readout, event building and recording of accepted events. Its proper functioning plays a critical role in the data-taking efficiency of the CMS experiment. In order to ensure high availability and recover promptly in the event of hardware or software failure of the subsystems, an expert system, the DAQ Expert, has been developed. It aims at improving the data taking efficiency, reducing the human error in the operations and minimising the on-call expert demand. Introduced in the beginning of 2017, it assists the shift crew and the system experts in recovering from operational faults, streamlining the post mortem analysis and, at the end of Run 2, triggering fully automatic recovery without human intervention. DAQ Expert analyses the real-time monitoring data originating from the DAQ components and the high-level trigger updated every few seconds. It pinpoints data flow problems, and recovers them automatically or after given operator approval. We analyse the CMS downtime in the 2018 run focusing on what was improved with the introduction of automated recovery; present challenges and design of encoding the expert knowledge into automated recovery jobs. Furthermore, we demonstrate the web-based, ReactJS interfaces that ensure an effective cooperation between the human operators in the control room and the automated recovery system. We report on the operational experience with automated recovery.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

SYMBIOSYS: A Methodology for Performance Analysis of Composable HPC Data Services

Microservices are a powerful new way of building, customizing, and deploying distributed services owing to their flexibility and maintainability. Several large-scale distributed platforms have emerged to serve the growing needs of data-centric workloads and services in commercial computing. Concurrently, high-performance computing (HPC) systems and software are rapidly evolving to meet the demands of diversified applications and heterogeneity. The interplay of hardware factors, software configuration parameters, and the flexibility offered with a microservice architecture makes it nontrivial to estimate the optimal service instantiation for a given application workload. Further, this problem is exacerbated when considering that these services operate in a dynamic and heterogeneous HPC environment. An optimally integrated service can be vastly more performant than a haphazardly integrated one. Existing performance tools for HPC either fail to understand the request-response model of communication inherent to microservices or they operate within a narrow scope, limiting the insight that can be gleaned from employing them in isolation. We propose a methodology for integrated performance analysis of HPC microservices frameworks and applications called SYMBIOSYS. We describe its design and implementation within the context of the Mochi framework. This integration is achieved by combining distributed callpath profiling and tracing with a performance data exchange strategy that collects fine-grained, low-level metrics from the RPC communication library and network layers. The result is a portable, low-overhead performance analysis setup that provides a holistic profile of the dependencies among microservices and how they interact with the Mochi RPC software stack. Using HEPnOS, a production-quality Mochi data service, we demonstrate the low-overhead operation of SYMBIOSYS at scale and use it to identify the root causes of poorly performing service configurations.

microservices↗
DOE OSTI2021

Nonlinear dynamics of topological Dirac fermions in 2D spin-orbit coupled materials

Abstract The graphene family materials are two-dimensional staggered monolayers with a gapped energy band structure due to intrinsic spin-orbit coupling. The mass gaps in these materials can be manipulated on-demand via biasing with a static electric field, an off-resonance circularly polarized laser, or an exchange interaction field, allowing the monolayer to be driven through a multitude of topological phase transitions. We investigate the dynamics of spin-orbit coupled graphene family materials to unveil topological phase transition fingerprints embedded in the nonlinear regime and show how these signatures manifest in the nonlinear Kerr effect and in third-harmonic generation processes. We show that the resonant nonlinear spectral response of topological fermions can be traced to specific Dirac cones in these materials, enabling characterization of topological invariants in any phase by detecting the cross-polarized component of the electromagnetic field. By shedding light on the unique processes involved in harmonic generation via topological phenomena our findings open an encouraging path towards the development of novel nonlinear systems based on two-dimensional semiconductors of the graphene family.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Computational capacity in hydrodynamic real-time hybrid simulation applied to simulate the dynamic response of floating offshore wind turbines

Real-time hybrid simulation (RTHS) mitigates similitude distortions in model-scale tests of floating offshore wind turbines (FOWTs) by coupling physical experiments with numerical models in real time. The coupling requires faster-than-real-time numerical computations to satisfy temporal similitude with the physical experiment, presenting a bottleneck for using more complex numerical models in RTHS. This paper presents a hydrodynamic-RTHS (hydro-RTHS) framework for FOWTs that simulates the hydrodynamics physically and the aerodynamics numerically with sensor feedback from the physical testing. The framework adapts the three-loop hardware architecture to leverage greater computational resources and mitigate strict temporal requirements, enabling more computationally demanding numerical analyses in hydro-RTHS. The three-loop hardware architecture integrates multiple machines, each dedicated to either numerical analysis or RTHS controls, with a rate-transition algorithm to synchronize the tasks executed across the different machine processors. Virtual and physical tests verified and validated the hydro-RTHS framework, respectively. The ”virtual” tests, which approximates the physical domain numerically, verified the RTHS framework with respect to a numerical full-scale complete FOWT model simulated in the open-source software, OpenFAST. The virtual tests were able to maintain comparable control signals while enabling greater computational resources for the numerical calculations. Real-world physical tests demonstrated that the hydro-RTHS framework computes aerodynamic forces similar to the complete OpenFAST model, validating the hydro-RTHS framework using the three-loop hardware architecture. Findings show that the hydro-RTHS framework with the three-loop hardware architecture is computationally efficient, with reserve capacity to simulate more complex problems due to the customized software, hardware, and rate-transition algorithm.

17 WIND ENERGY↗
DOE OSTI2025

Autonomous phase mapping of gold nanoparticles synthesis with differentiable models of spectral shape

Autonomous experimentation–or self-driving labs–offers a systematic approach to accelerate materials discovery by integrating automated synthesis, characterization, and data-driven decision-making. We present a closed-loop workflow for the on-demand synthesis and structural characterization of colloidal gold nanoparticles, enabling direct mapping from composition to nanoscale structure. Our framework leverages differentiable models of spectral shape to address two central tasks in self-driving labs: (a) phase mapping, or identifying compositional regions with distinct structural behavior; and (b) material retrosynthesis, or optimizing compositions for target structure. Using functional data analysis, we develop a data-driven model with generative pre-training, active learning, and high-throughput experiments to predict spectral responses across composition space. We demonstrate the approach on seed-mediated growth of gold nanoparticles, showcasing its ability to extract design rules, reveal secondary interactions, and efficiently navigate morphology space. Gradient-based optimization of the models enables inverse design, making this a unified platform.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Temporal dynamics of free‐living nitrogen fixation in the switchgrass rhizosphere

Abstract Free‐living nitrogen fixation (FLNF) represents an important terrestrial N source and is gaining interest for its potential to contribute plant available N to bioenergy cropping systems. Switchgrass, a cellulosic bioenergy crop, may be particularly reliant on FLNF when grown on low N systems, like marginal lands. However, the potential contributions of FLNF to switchgrass as well as the controls on this process are not well understood. In this study, we evaluated drivers of FLNF rates and N‐fixing microbial (diazotrophic) community composition in field‐grown switchgrass systems over two growing seasons with high temporal sampling. We found that climate variables are strong drivers of FLNF rates in switchgrass systems, compared to other environmental and biological factors including soil nutrients and diazotrophic community composition. Increased soil moisture availability generally promoted FLNF rates, but extreme rainfall events were detrimental. These climate‐related responses suggest a potential for loss of FLNF‐derived N contributions under projected climate shifts. We found a significant, but weak correlation between diazotrophic community composition and FLNF rates. We also observed a significant shift in the diazotrophic community composition between 2017 and 2018 and similarly measured a significant difference in FLNF rates between growing seasons. Lastly, we found that seasonal FLNF N contributions, based on measurement with high temporal resolution, has the potential to meet up to 80% of switchgrass N demands suggesting that FLNF measurements extrapolated from fewer time points or locations may underestimate these potential N contributions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

PV-Fed Smart Inverters for Mitigation of Voltage and Frequency Fluctuations in Islanded Microgrids

The presence of low-inertia distributed generation (DG) units makes islanded microgrids vulnerable to voltage and frequency variations. Besides, the considerable difference between the inertia of synchronous-based and inverter-based DGs results in a power imbalance between supply and demand during abnormal conditions. As a result, both voltage and frequency of microgrid ac-bus start oscillating which might have severe consequences such as blackouts. This paper deploys the traditional controller of photovoltaic (PV) units to improve the dynamic behavior of islanded microgrids by suppressing the voltage and frequency fluctuations. To this end, an adaptive piecewise droop (APD) characteristic is proposed and employed in PV units to attain a faster balance between generation and consumption during transients, leading to an enhanced frequency response. Besides, the reactive-power control loop is equipped with a droop characteristic which enables the PV units to inject/absorb reactive power during transients and participate in voltage-profile enhancement of the system. Case study results are presented using PSCAD/EMTDC to confirm the validity of proposed method in improving the dynamics of islanded microgrids.

Pilehvar, Mohsen S.↗
DOE OSTI2024

A Review of the Multiple-Readout Concept and Its Application in an Integrally Active Calorimeter

A comprehensive multi-jet physics program is anticipated for experiments at future colliders. Key physics processes necessitate detectors that can distinguish signals from W and Z bosons and the Higgs boson. Typical examples include channels with or pairs and processes involving new physics in those cases where neutral particles must be disentangled from charged ones due to the presence of W or Z bosons in their final states. Such a physics program demands calorimetric energy resolution at or beyond the limits of traditional calorimetric techniques. Multiple-readout calorimetry, which aims to reduce fluctuations in energy measurements of hadronic showers, is a promising approach. The first part of this article reviews dual- and triple-readout calorimetry within a mathematical framework describing the underlying compensating mechanism. The second part proposes a potential implementation using an integrally active and total absorption detector. This model serves as the basis for several Monte Carlo studies, illustrating how the response of a multiple-readout calorimeter depends on construction parameters. Among the layouts considered, one configuration operating in triple-readout mode shows the potential to achieve an energy resolution approaching .

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

The hydrogen economy can reduce costs of climate change mitigation by up to 22%

In response to the urgent need to mitigate climate change via net-zero targets, many nations are renewing their interest in clean hydrogen as a net-zero energy carrier. Although clean hydrogen can be directly used in various sectors for deep decarbonization, the relatively low energy density and high production costs have raised doubts as to whether clean hydrogen development is worthwhile. Here, we improve on the GCAM model by including a more comprehensive and detailed representation of clean hydrogen production, distribution, and demand in all sectors of the global economy and simulate 25 scenarios to explore the cost-effectiveness of integrating clean hydrogen into the global energy system. We show that, due to costly technical obstacles, clean hydrogen can only provide 3%–9% of the 2050 global final energy use. Nevertheless, clean hydrogen deployment can reduce overall energy decarbonization costs by 15%–22%, mainly via powering “hard-to-electrify” sectors that would otherwise face high decarbonization expenditures. Our work provides practical references for cost-effective clean hydrogen planning.

08 HYDROGEN↗
DOE OSTI2021

Machine learned features from density of states for accurate adsorption energy prediction

Materials databases generated by high-throughput computational screening, typically using density functional theory (DFT), have become valuable resources for discovering new heterogeneous catalysts, though the computational cost associated with generating them presents a crucial roadblock. Hence there is a significant demand for developing descriptors or features, in lieu of DFT, to accurately predict catalytic properties, such as adsorption energies. Here, we demonstrate an approach to predict energies using a convolutional neural network-based machine learning model to automatically obtain key features from the electronic density of states (DOS). The model, DOSnet, is evaluated for a diverse set of adsorbates and surfaces, yielding a mean absolute error on the order of 0.1 eV. In addition, DOSnet can provide physically meaningful predictions and insights by predicting responses to external perturbations to the electronic structure without additional DFT calculations, paving the way for the accelerated discovery of materials and catalysts by exploration of the electronic space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Advancing uncertainty characterization for understanding projected water scarcity in multi-sector, multi-actor river basins across scales.

Invited Talk at AGU Fall Meeting 2023 Modeling how human institutions and infrastructure interact with the water cycle is essential to better understand the vulnerability, and resilience of water resources systems from the local to the global scale. This is especially true when investigating multi-sector, multi-actor responses to the effects of long-term changes and short-term shocks. It is also well recognized that uncertainty present throughout the modeling cycle (e.g., in data, functional relations, and model coupling approaches) limits our ability to trace the interactive dynamics of human-water systems, as well as to quantify their implications for management and planning. Systems with large numbers of diverse stakeholders further compound this challenge, as uncertain drivers and complex dynamics might have very disparate effects on water users. The work presented is conducted through the Integrated Multisector Multiscale Modeling (IM3) Science Focus Area, which explores how human and natural systems co-evolve in response to change. Through this multi-year effort, we have developed exploratory modeling methods to better characterize how the uncertain human and natural drivers of water scarcity yield consequential vulnerabilities in institutionally complex, multi-sectoral systems. This talk will specifically present on a series of uncertainty characterization experiments performed in the Upper Colorado River Basin, a sub-basin of the Colorado with thousands of water users. These complementary and systematic experiments are aimed at understanding: How are various uncertain stressors (e.g., climate change, demand growth) affecting the diverse water users of this basin in terms of water shortage? What are the key drivers of this shortage for each user? Methods and results from this work are used to address additional questions on the ability of adaptation to modulate the effects of these uncertain drivers, and on their compounding effects across spatial scales and through sectoral interactions.

Hadjimichael, Antonia↗
DOE OSTI2021

MULTI-MODAL global surveillance methodology for predictive and on-demand characterization of localized processes using cube satellite platforms and deep learning techniques

This paper presents the work completed towards the development of a multi-modal global surveillance methodology using cube satellite (CubeSat) platforms and novel data analysis techniques. A CubeSat system equipped with adequate sensors and data analytics capabilities can autonomously characterize various phenomena of interest on the Earth’s surface. CubeSats are advantageous over conventional satellites in certain remote monitoring applications because of their reduced construction costs (due to the availability of commercially-off-the-shelf components) and are easier to launch. The CubeSat surveillance system developed in this paper focused on phenomena of interest surrounding the nuclear fuel cycle in support of nuclear non-proliferation and emergency response. To observe the phenomena, a constellation of 3U and 6U CubeSats deployed from the ISS with adequate components was chosen. Four different sensor configurations were identified for remote sensing: panchromatic/multispectral in the visible and near-infrared spectrum, multispectral in infrared spectrum, hyperspectral in infrared spectrum, and multispectral in ultraviolet spectrum. While a panchromatic/multispectral sensor configuration has CubeSat flight heritage at the required spatial resolutions, the other three sensor types need future 3 development to meet signature and system requirements. Once each sensor onboard the CubeSat system collects data on a target of interest, the onboard computers would then apply the deep learning-based characterization methodology developed in this paper to identify phenomena. Four surrogate datasets containing representative simplified “images” were created for each sensor type to train the characterization methodology. A convolutional neural network was applied to each dataset and produced recall rates for the phenomena between 89.7% - 99.3% and precision rates between 92.3% - 99.9%. Each phenomenon’s presence probability from each network is then combined into a final characterization solution for a target area. This paper covers multiple interdisciplinary areas to develop the foundation for a CubeSat surveillance system focused on phenomena surrounding the nuclear fuel cycle.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Emerging technologies and radical collaboration to advance predictive understanding of watershed hydrobiogeochemistry

Increasing population and resource-intensive lifestyles are driving enhanced demands for clean water, food, and energy. In parallel, land-use change, climate change, and perturbations—including drought, floods, fires, and early snowmelt—are significantly reshaping interactions within watersheds throughout the world. While watersheds are the Earth's key functional unit for assessing and managing water resources, hydrological processes in watersheds also mediate biogeochemical interactions that support terrestrial life on Earth (Kaushal, Gold, Bernal, & Tank, 2018; National Research Council, 2012). Although society is dependent upon clean water availability, tractable prediction of watershed hydrobiogeochemical behavior, including watershed response to perturbations, remains a challenge. Central to the challenge are complex, multiscale interactions between plants, microorganisms, organic matter, minerals, dissolved constituents, and migrating fluids, which occur within and across bedrock-to-canopy compartments and along extensive lateral gradients of a watershed. Several recent community reports have synthesized formidable challenges associated with watershed science and technology (AGU, 2018; Blöschl et al., 2019). Here, we discuss emerging technologies and collaboration modes that are critical for developing generalizable insights about and predictive understanding of complex watershed hydrobiogeochemical behavior, which are important for underpinning optimized natural resource management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Thermal Dynamics and Lithium Plating Detection in High‐Power Li‐Ion Batteries for eVTOL Applications

The rapid electrification of aerial transportation is driving the need for high‐performance Li‐ion batteries that can operate reliably under stringent thermal and safety constraints. The unique mission profile of electric Vertical Take‐off and Landing (eVTOL) aircraft necessitates a focused investigation into the thermal behavior and safety characteristics of these batteries. Here, in this study, operando isothermal microcalorimetry is employed to examine the thermal evolution of Li‐ion batteries under cycling conditions representative of eVTOL operations. These findings reveal that high‐power discharge events—such as those during take‐off and landing—shift the thermal response toward exothermic behavior, in contrast to the typically endothermic response expected under near‐equilibrium cycling conditions. Additionally, the results suggest that advanced electrolyte formulations may help suppress excess heat generation, thereby improving battery safety. Notably, the calorimetric results exhibit a distinct thermal signature associated with lithium plating, offering a potential diagnostic for detecting Li plating during eVTOL operation. Overall, this study demonstrates the utility of isothermal microcalorimetry as a valuable tool for assessing thermal risks in Li‐ion batteries for eVTOL applications, and highlights the importance of targeted design strategies to mitigate safety hazards during high‐power demand scenarios.

Li plating↗
DOE OSTI2023

The role of corporate investment in start-ups for climate-tech innovation

Enabling and accelerating the full potential of energy innovation is a critical component of the global policy response to climate change. Incentivizing start-ups advancing climate-tech (i.e., products and services related to clean energy and climate change), has become central to innovation policy because start-ups are nimble (compared to incumbents), can quickly focus on bringing new technologies to market, and simultaneously create new jobs and catalyze local industries. Public policies to support climate-tech start-ups and efforts such as Mission Innovation tend to focus on increasing government spending (e.g., grants for research and development) or on creating demand pull for new technologies (e.g., feed-in-tariffs). But to go from research and development to widespread adoption of new products and services, start-ups need to grow and scale, and this requires support from private investors. Yet, research on how different investors can shape the direction of climate innovation—and how public policy can incentivize private investment in climate-tech start-ups to support the public good—is surprisingly sparse.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗