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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.

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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Understanding the perceptions, roles and interactions of stakeholder networks managing health-care waste: A case study of the Gaza Strip

Highlights: • We systematically review scientific literature about health-care waste management. • We identify and discuss gaps, trends, efforts, and key-factors. • We suggest areas for improvement and best practices reported in literature. • We include recommendations for policy makers, practitioners and researchers. • We promote a holistic and harmonized approach to health-care waste management. - Abstract: The sustainable management of waste requires a holistic approach involving a range of stakeholders. What can often be difficult is to understand the manner in which different types of stakeholder networks are composed and work, and how best to enhance their effectiveness. Using social network analysis and stakeholder analysis of healthcare waste management stakeholders in the case study region of the Gaza Strip, this study aimed to understand and examine the manner in which the networks functioned. The Ministry of Health was found to be the most important stakeholder, followed by municipalities and solid waste management councils. Some international agencies were also mentioned, with specific roles, while other local institutions had a limited influence. Finally while health-care facilities had a strong interest in waste management, they were generally poorly informed and had limited links to each other. The manner in which the networks operated was complicated and influenced by differences in perception, sharing of information, access to finance and levels of awareness. The lack of a clear legal framework generated various mistakes about roles and responsibilities in the system, and evidently regulation was not an effective driver for improvement. Finally stakeholders had different priorities according to the waste management issues they were involved with, however segregation at the source was identified as a key requirement by most. Areas for improving the effectiveness of the networks are suggested. The analysis utilized an innovative methodology, which involved a large number of stakeholders. Such an approach served to raise interest and awareness at different levels (public authorities, health providers, supporting actors, others), stimulate the discussion about the adoption of specific policies, and identify the effective way forward.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Combining anaerobic digestion and hydrothermal liquefaction in the conversion of dairy waste into energy: A techno economic model for New York state

There is increasing global interest and policies being enacted to lower greenhouse gas emissions, especially from the agricultural sector. In the U.S. for example, states with large dairy operations may combine proven manure valorization technologies, such as anaerobic digestion and hydrothermal liquefaction. Sustainable manure treatment would increase the recovery of energy and other useful co-products, namely biogas, biocrude oil and hydro-char as well as lower the environmental impacts. In this study, the economic feasibility of implementing a centralized bioenergy system in New York State was investigated. Here, the feasibility of this transformation depends on many factors, including capital costs, discount rates, and other financing arrangements, electricity selling prices, incentives and farm sizes and locations. For a large-scale implementation in New York State accounting for nearly 50% of the state’s dairy farms, our model of a distributed, hybrid anaerobic/hydrothermal system was shown to treat 590 million liters of wet manure per day, producing 607 million kWh of electricity, 162,000 L of biocrude oil and 117,000 kg of hydro-char per day. Electricity selling price is a critical factor. Increasing the electricity selling price from wholesale ($\$0.06$ /kWh) to retail ($\$0.18$ kWh) increased the net present value from $\$395$ million to $1.5 billion (considering a 40-year project lifetime).

42 ENGINEERING↗
DOE OSTI2022

Optimizing Earthquake Nowcasting With Machine Learning: The Role of Strain Hardening in the Earthquake Cycle

Abstract Nowcasting is a term originating from economics, finance, and meteorology. It refers to the process of determining the uncertain state of the economy, markets or the weather at the current time by indirect means. In this paper, we describe a simple two‐parameter data analysis that reveals hidden order in otherwise seemingly chaotic earthquake seismicity. One of these parameters relates to a mechanism of seismic quiescence arising from the physics of strain‐hardening of the crust prior to major events. We observe an earthquake cycle associated with major earthquakes in California, similar to what has long been postulated. An estimate of the earthquake hazard revealed by this state variable time series can be optimized by the use of machine learning in the form of the Receiver Operating Characteristic skill score. The ROC skill is used here as a loss function in a supervised learning mode. Our analysis is conducted in the region of 5° × 5° in latitude‐longitude centered on Los Angeles, a region which we used in previous papers to build similar time series using more involved methods (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020EA001097 ; Rundle, Donnellan et al., 2021, https://doi.org/10.1029/2021EA001757 ; Rundle, Stein et al., 2021, https://doi.org/10.1088/1361-6633/abf893 ). Here we show that not only does the state variable time series have forecast skill, the associated spatial probability densities have skill as well. In addition, use of the standard ROC and Precision (PPV) metrics allow probabilities of current earthquake hazard to be defined in a simple, straightforward, and rigorous way.

58 GEOSCIENCES↗
DOE OSTI2014

Post-2020 climate agreements in the major economies assessed in the light of global models

Integrated assessment models can help in quantifying the implications of international climate agreements and regional climate action. This paper reviews scenario results from model intercomparison projects to explore different possible outcomes of post-2020 climate negotiations, recently announced pledges and their relation to the 2°C target. We provide key information for all the major economies, such as the year of emission peaking, regional carbon budgets and emissions allowances. We highlight the distributional consequences of climate policies, and discuss the role of carbon markets for financing clean energy investments, and achieving efficiency and equity.

Tavoni, Massimo↗
DOE OSTI2021

Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality

Abstract Deep neural networks (‘deep learning’) have emerged as a technology of choice to tackle problems in speech recognition, computer vision, finance, etc. However, adoption of deep learning in physical domains brings substantial challenges stemming from the correlative nature of deep learning methods compared to the causal, hypothesis driven nature of modern science. We argue that the broad adoption of Bayesian methods incorporating prior knowledge, development of solutions with incorporated physical constraints and parsimonious structural descriptors and generative models, and ultimately adoption of causal models, offers a path forward for fundamental and applied research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Near-term transition and longer-term physical climate risks of greenhouse gas emissions pathways

Policy, business, finance and civil society stakeholders are increasingly looking to compare future emissions pathways across both their associated physical climate risks stemming from increasing temperatures and their transition climate risks stemming from the shift to a low-carbon economy. Herein, we present an integrated framework to explore near-term (to 2030) transition risks and longer-term (to 2050) physical risks, globally and in specific regions, for a range of plausible greenhouse gas emissions and associated temperature pathways, spanning 1.5–4 °C levels of long-term warming. By 2050, physical risks deriving from major heatwaves, agricultural drought, heat stress and crop duration reductions depend greatly on the temperature pathway. By 2030, transition risks most sensitive to temperature pathways stem from economy-wide mitigation costs, carbon price increases, fossil fuel demand reductions and coal plant capacity reductions. Considering several pathways with a 2 °C target demonstrates that transition risks also depend on technological, policy and socio-economic factors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The impact of policies and business models on income equity in rooftop solar adoption

Low- and moderate-income (LMI) households are less likely to adopt rooftop solar photovoltaics (PVs) than higher-income households in the United States. As the existing literature has shown, this dynamic can decelerate rooftop PV deployment and has potential energy justice implications, in light of the cost-shifting between PV and non-PV households that can occur under typical rate structures and incentive programmes. Here we show that some state policy interventions and business models have expanded PV adoption among LMI households. Additionally, we find evidence that LMI-specific financial incentives, PV leasing and property-assessed financing have increased the diffusion of PV adoption among LMI households in existing markets and have driven more installations into previously underserved low-income communities. By shifting deployment patterns, we posit that these interventions could catalyse peer effects to increase PV adoption in low-income communities even among households that do not directly benefit from the interventions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Complexity-calibrated benchmarks for machine learning reveal when prediction algorithms succeed and mislead

Abstract Recurrent neural networks are used to forecast time series in finance, climate, language, and from many other domains. Reservoir computers are a particularly easily trainable form of recurrent neural network. Recently, a “next-generation” reservoir computer was introduced in which the memory trace involves only a finite number of previous symbols. We explore the inherent limitations of finite-past memory traces in this intriguing proposal. A lower bound from Fano’s inequality shows that, on highly non-Markovian processes generated by large probabilistic state machines, next-generation reservoir computers with reasonably long memory traces have an error probability that is at least $$\sim 60\%$$ ∼ 60 % higher than the minimal attainable error probability in predicting the next observation. More generally, it appears that popular recurrent neural networks fall far short of optimally predicting such complex processes. These results highlight the need for a new generation of optimized recurrent neural network architectures. Alongside this finding, we present concentration-of-measure results for randomly-generated but complex processes. One conclusion is that large probabilistic state machines—specifically, large $$\epsilon$$ ϵ -machines—are key to generating challenging and structurally-unbiased stimuli for ground-truthing recurrent neural network architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The potential of carbon markets to accelerate green infrastructure based water quality trading

Green infrastructure solutions can improve in-stream water quality in lieu of building electricity-consuming gray infrastructure. Permitted under the United States Clean Water Act, these programs allow regulated utilities to trade point-source water quality obligations with non-point source mitigation efforts in the watershed. Carbon financing can provide an incentive for water quality trading. Here we combine data on impaired waters, treatment technologies, and life cycle greenhouse gas emissions in the Contiguous United States, and compare traditional treatment technologies to alternative green infrastructure. We find green infrastructure could save $\$15.6$ billion dollars, 21.2 terawatt-hours of electricity, and 29.8 million tonnes of carbon dioxide equivalent emissions per year while sequestering over 4.2 million tonnes CO2e per year over a 40 year time horizon. Green infrastructure solutions may have the potential to generate $\$679$ million annually in carbon credit revenue (at $\$20$ per credit), which represents a unique opportunity to help accelerate water quality trading.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

A Comprehensive View of Global Potential for Hydro-generated Electricity

In this study, we assess global hydropower potential using runoff and stream flow data, along with turbine technology performance, cost assumptions, and environmental considerations. The results provide the first comprehensive quantification of global hydropower potential including gross, technical, economic, and exploitable estimates. Total global potential of gross, technical, economic, and exploitable hydropower are estimated to be approximately 128, 39, 32, and 27 petawatt hours per year, respectively. The economic and exploitable potential of hydropower are calculated at less than 9 cents/kWh. We find that hydropower has the potential to supply a significant portion of the world energy needs, although this potential varies substantially by region. Globally, hydropower can potentially supply about 1.5 times the total electricity demand in 2005. Estimated hydropower resources in a number of countries are sufficient to accommodate their demand for electricity in 2005, e.g., Brazil (5.6 times), Russia (4.6 times), and Canada (3.5 times). A sensitivity analysis indicates that hydropower estimates are not highly sensitive to five key parameters: design flow (varying by -2% to +1% at less than 9 cents/kWh), cost and financing options (by -7% to +6%), turbine efficiency (by -10% to +10%), stream flow (by -10% to +10%), and fixed charge rate (by -6% to 5%). This sensitivity analysis emphasizes the reliable role of hydropower for future energy systems, when compared to other renewable energy resources with larger uncertainty in their future potentials.

Zhou, Yuyu↗
DOE OSTI2019

CSP-Plant Modeling Guidelines and Compliance of the System Advisor Model (SAM)

Accurately modeling risks, costs, and electricity output is essential to the financing and advancement of concentrating solar power projects. To address this need, a group of CSP experts created a guideline document, titled SolarPACES Guideline for Bankable STE Yield Assessment [1]. To make this information more accessible and allow stakeholders to test specific models against the recommendations, the guidelines have been condensed into a spreadsheet-based checklist. The checklist was applied to NREL's System Advisor Model (SAM) software, providing useful feedback to both the checklist group and the SAM development team. This study showed strong agreement between SAM and the guidelines, demonstrated the use of the guidelines in model validation, and resulted in several recommended improvements to SAM.

14 SOLAR ENERGY↗
DOE OSTI2020

CSP Plant Construction, Start-Up, and O&M Best Practices Study

There are currently 90 operational CSP plants of 10 MW or larger located throughout the world, primarily in Spain, U.S., MENA region, South Africa, and China. In 2018, the U.S. Department of Energy (DOE) funded Solar Dynamics, under subcontract to NREL, to investigate and document lessons learned and best practices associated with the construction, start-up, and operations phases of a majority of these plants. DOE funding for the effort was leveraged by additional funds provided through SolarPACES and the World Bank. This information was obtained by interviewing key stakeholders representing CSP plant owners, operators, construction companies, vendors, technical advisors and financers of these systems, and will be published as an NREL Technical Report at the completion of the project. This paper provides an overview of key challenges facing today's CSP parabolic trough and power tower technologies during all the phases mentioned above.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2025

Hybrid renewable energy systems

In the pursuit of ecologically sustainable and resilient energy systems, increasingly more attention is being devoted to a diversity of energy generation and storage methods. As the landscape of generation technology gains nuance and complexity, a wide-ranging set of technical questions has emerged, touching on topics that range from control and optimization of hybrid systems to finance and economic viability to multi-fidelity modeling and scientific machine learning. In the context of this special issue, hybrid renewable energy systems are any systems that consider the combined dynamics of more than one form of generation, storage, or grid subsystem. Research endeavors have delved into improving the flexibility of energy systems by utilizing existing resources, introducing novel operational strategies, deploying enhanced renewable forecasts, and exploring emerging technologies. In conclusion, the interconnection among various sectors has garnered heightened attention, not only due to the provision of additional tradable energy products but also for furnishing flexible headroom to system operators.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Solving high-dimensional partial differential equations using deep learning

Developing algorithms for solving high-dimensional partial differential equations (PDEs) has been an exceedingly difficult task for a long time, due to the notoriously difficult problem known as the “curse of dimensionality.” This paper introduces a deep learning-based approach that can handle general high-dimensional parabolic PDEs. To this end, the PDEs are reformulated using backward stochastic differential equations and the gradient of the unknown solution is approximated by neural networks, very much in the spirit of deep reinforcement learning with the gradient acting as the policy function. Numerical results on examples including the nonlinear Black–Scholes equation, the Hamilton–Jacobi–Bellman equation, and the Allen–Cahn equation suggest that the proposed algorithm is quite effective in high dimensions, in terms of both accuracy and cost. Furthermore, this opens up possibilities in economics, finance, operational research, and physics, by considering all participating agents, assets, resources, or particles together at the same time, instead of making ad hoc assumptions on their interrelationships.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

An overview of the fusion landscape

Fusion is very attractive as a potential energy source, but it is taking a long time to develop into a commercial reality. Given the challenges of climate change and the need for dispatchable power as a complement to renewable energy sources that vary on daily and seasonal timescales, there is great enthusiasm internationally to accelerate the commercialization of fusion energy. Forty-five private companies around the globe, with total financing of $7.1 billion, are engaged in the development of fusion energy. In the United States, the White House has put forward a “Bold Decadal Vision for Commercial Fusion Energy,” with bipartisan support from Congress. To develop commercial fusion energy, certain goals must be met. In conclusion, a wide variety of approaches are being pursued to meet these goals.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

A formal method for identifying distinct states of variability in time-varying sources: SGR A* as an example

Continuously time variable sources are often characterized by their power spectral density and flux distribution. These quantities can undergo dramatic changes over time if the underlying physical processes change. However, some changes can be subtle and not distinguishable using standard statistical approaches. Here, we report a methodology that aims to identify distinct but similar states of time variability. We apply this method to the Galactic supermassive black hole, where 2.2 μm flux is observed from a source associated with Sgr A* and where two distinct states have recently been suggested. Our approach is taken from mathematical finance and works with conditional flux density distributions that depend on the previous flux value. The discrete, unobserved (hidden) state variable is modeled as a stochastic process and the transition probabilities are inferred from the flux density time series. Using the most comprehensive data set to date, in which all Keck and a majority of the publicly available Very Large Telescope data have been merged, we show that Sgr A* is sufficiently described by a single intrinsic state. However, the observed flux densities exhibit two states: noise dominated and source dominated. Our methodology reported here will prove extremely useful to assess the effects of the putative gas cloud G2 that is on its way toward the black hole and might create a new state of variability.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Tokamak Energy’s pre-concept design for a fusion power plant: an overview of ST-E1

Climate change and rapidly rising energy demand, driven in part by artificial intelligence and data-centre growth, create an urgent need for stable, low-carbon, and abundant power. Fusion is a promising long-term solution, yet its commercialisation faces a fundamental paradox in today’s investment environment: pilot plants are essential to de-risk physics, engineering, and operations, but their limited lifetime energy output and high upfront costs make them difficult to finance. This paper presents Tokamak Energy’s response: ST-E1, a pre-concept design for a low-aspect-ratio tokamak power plant engineered specifically to overcome this challenge. ST-E1 is designed from the outset for phased operation—pilot and commercial phases, with an upgrade phase in between—with emphasis on commercial viability, maintainability, nuclear engineering, modularity, and upgradability. A key design principle is the deliberate separation of long-lived assets, such as the magnet cage and vacuum vessel, from replaceable in-vessel systems. This provides an attractive and credible investment approach to generate operational data and de-risk key technologies while preserving most capital-intensive assets for later commercial phases. The architecture supports continuous optimisation toward high net electric power (targeting 800–1000 MW net electric), a normalised capital expenditure of $\$$ 12–14k/kW of net electric power, and high availability (targeting > 80%). A tokamak core with a 5 m major radius, aspect ratio of 2.3, and on-plasma axis toroidal field of 5.25 T was selected to meet these objectives. This paper summarises the ST-E1 design philosophy, principal features, and development methodology. It introduces a Focus Collection of 11 papers detailing the pre-concept design of the entire tokamak and corresponding plant.

ST-E1↗
DOE OSTI2022

Quantifying the regional stranded asset risks from new coal plants under 1.5 °C

Momentum to phase out unabated coal use is growing globally. This transition is critical to meeting the Paris climate goals but can potentially lead to large amounts of stranded assets, especially in regions with newer and growing coal fleets. Here we combine plant-level data with a global integrated assessment model to quantify changes in global stranded asset risks from coal-fired power plants across regions and over time. With new plant proposals, cancellations, and retirements over the past five years, global net committed emissions in 2030 from existing and planned coal plants declined by 3.3 GtCO 2 (25%). While these emissions are now roughly in line with initial Nationally Determined Contributions (NDCs) to the Paris Agreement, they remain far off track from longer-term climate goals. Progress made in 2021 towards no new coal can potentially avoid a 24% (503 GW) increase in capacity and a 55% ($520 billion) increase in stranded assets under 1.5 °C. Stranded asset risks fall disproportionately on emerging Asian economies with newer and growing coal fleets. Recent no new coal commitments from major coal financers can potentially reduce stranding of international investments by over 50%.

54 ENVIRONMENTAL SCIENCES↗