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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

An ensemble data assimilation modeling system for operational outdoor microalgae growth forecasting

Microalgae have received increasing attention as a potential feedstock for biofuel or biobased products. Forecasting the microalgae growth is beneficial for managers in planning pond operations and harvesting decisions. This study proposed a biomass forecasting system comprised of the Huesemann Algae Biomass Growth Model (BGM), the Modular Aquatic Simulation System in Two Dimensions (MASS2), ensemble data assimilation (DA), and numerical weather prediction Global Ensemble Forecast System (GEFS) ensemble meteorological forecasts. The novelty of this study is to seek the use of ensemble DA to improve both BGM and MASS2 model initial conditions with the assimilation of biomass and water temperature measurements and consequently improve short-term biomass forecasting skills. This study introduces the theory behind the proposed integrated biomass forecasting system, with an application undertaken in pseudo-real-time in three outdoor ponds cultured with Chlorella sorokiniana in Delhi, California, United States. Results from all three case studies demonstrate that the biomass forecasting system improved the short-term (i.e., 7-day) biomass forecasting skills by about 60% on average, comparing to forecasts without using the ensemble DA method. Given the satisfactory performances achieved in this study, it is probable that the integrated BGM-MASS2-DA forecasting system can be used operationally to inform managers in making pond operation and harvesting planning decisions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Real-time ensemble microalgae growth forecasting with data assimilation

Accurate short-range (e.g., 7-day) microalgae growth forecasts will be beneficial for both production and harvesting of microalgae. This study developed an operational microalgae growth forecasting system with ensemble data assimilation (DA). The forecasting system was validated against observed Monoraphidium minutum 26B-AM growth in two outdoor pond cultures located in Mesa, Arizona, U.S. We first examined the relative roles of uncertainty in the meteorological forecast and initial conditions (i.e., algal concentration at the time of forecast) in the microalgae 7-day forecast and found initial conditions dominated the microalgae forecasting skill, suggesting the importance of implementing DA to improve initial condition characterization. To correct the systematic bias in biomass simulations, we developed a particle filter with bias estimation (PFBE) DA method to estimate biases and correct the model forecast. We found the DA forecasting system could improve the 7-day microalgae forecasting skill by about 85% on average compared to model forecasts without DA. These results suggest the potential accuracy of biomass growth forecasts may be sufficient to inform real-time operational decisions, such as harvesting planning, for commercial-scale microalgae production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Timber Harvesting Economics in Early Commercial Thinning and Group Selection Prescriptions

Forest management decisions that consider both short- and long-term objectives are becoming more challenging and costly, due to market uncertainty, labor shortages, and the evolving forest industry. Under such unpredictable circumstances, a better understanding of the production economics of various silvicultural prescriptions is crucial in implementing sound forest management activities. The objectives of this study were to: a) estimate stump to landing productivity and cost for Early Commercial Thinning (ECT) and Group Selection (GS) prescriptions; and b) compare the harvest system cost ($/PMH) using machine rate and PATH (Planning and Analysis in Timber Harvesting) methods. Detailed time-motion studies were conducted in the spruce-fir forest of Northern Maine, USA, during the summer of 2019. The results, as expected, indicated the harvesting costs were 23% higher for ECT (21.08 $/m3) compared to GS (16.32 $/m3). The average productivity of the harvester and forwarder were reduced by 15 and 37%, respectively, for ECT compared to GS. The estimated harvesting system cost ($/PMH) was 4% higher as indicated by PATH compared to machine rate method. Higher operational productivity in GS can be attributed to the larger volume of logs, and shorter travel loaded distance than ECT. These findings can help forest managers make more informed decisions while implementing appropriate silvicultural prescriptions under varying conditions based on management objectives and market conditions.

Forestry↗
DOE OSTI2024

Predictive model using artificial neural network to design phase change material-based ocean thermal energy harvesting systems for powering uncrewed underwater vehicles

Uncrewed Underwater Vehicles (UUVs) are a major beneficiary of the phase change material (PCM)-based ocean thermal energy harvesting technology for their mission needs. However, this technology relies on different parameters and energy conversion steps that could be critical to the general energy generation efficiency. Sea trials showed that the design performed lower than their laboratory design specifications. This underperformance results from different factors, mainly the UUV’s trajectory, travel time, underwater ocean currents, temperature fluctuations, and biofouling on the heat exchanger due to long term underwater operations. Therefore, there exists a need to continuously monitor the ambient energy harvesting system and predict system performance, for mission planning purposes. Two major parameters influencing the energy harvesting system include the final pressure inside the hydraulic energy storage vessel or accumulator, and the electrical load value. Here, this work focuses on the hydraulic to electric energy conversion system. Therefore, a combination of numerical model and experimental testing is used to develop a predictive model using artificial neural network using MATLAB. After validation with experimental testing, 1000 data samples obtained from the numerical model are used to train the ANN. Compared to the experimental results, the developed ANN model can predict in less than a second the designed benchtop system’s total efficiency with less than 15 percent maximum error range. This predictive model development represents a cost-effective way for optimization and a computational energy efficient mode aboard UUVs for mission planning for deployed UUVs using PCM-based ocean thermal energy harvesting technology.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Optimizing Selection Pressures and Pest Management to Maximize Cultivation Yield (OSPREY) (Final Technical Report)

This project was proposed in response to AOI 1, Cultivation Intensification Processes for Algae, within the FY19 Bioenergy Technologies Office Multi-Topic Funding Opportunity Announcement (FOA Number: DE-FOA-0002029). The work was designed to address a critical industry need to improve annualized productivity, stability, and quality of algal production strains for biofuels and bioproducts. The overall project goals were to generate process innovations rooted in established outdoor systems for strain selection, improvement, maintenance, and cultivation as well as pest detection and tracking. Planned advances included a 50% improvement in harvest yield based on AFDW (g m 2 d -1 ), 50% improvement in robustness based on stability metrics (e.g., high-productivity cultivation days, pond uptime), and 20% improvement in conversion yield. Individually, each of our planned process improvements (e.g., pest tracking) had the potential to increase productivity. However, to realize increases in yield at the system level, improvements to one unit’s process must be balanced against potential effects on other processes. For example, changes to strains, cultivation, and pest management developed in isolation may hurt other unit operations. Therefore, a critical success factor of the project was the integration of the pipeline components, achieved through iterative field-to- (short term) lab testing. In addition, through sustainability models, we evaluated how improvements would alter industry scenarios.

09 BIOMASS FUELS↗
DOE OSTI2023

Integrated path planning and control through proximal policy optimization for a marine current turbine

This paper presents an integrated path planning and tracking control framework for a marine current turbine (MCT), where the MCT is treated as an energy-harvesting autonomous underwater vehicle (AUV). Considering the ocean (space of action) is continuous, the proposed framework employs two modules to address path planning and path tracking enabled by the proximal policy optimization (PPO) algorithm, which is a policy gradient deep reinforcement learning (RL) method. Further, to enable fully autonomous operation in a stochastic oceanic environment, the proposed path planning seeks a primary objective of maximizing the harvested energy; then, the path tracking module is designed to minimize the tracking error and avoid collisions with static and dynamic obstacles. Using field-collected acoustic Doppler current profiler (ADCP) data, the performance of the proposed framework is evaluated. Comparative studies with baseline algorithms in three different scenarios of path planning, path tracking without an obstacle, and path tracking with collision avoidance verify the effectiveness of our proposed approach.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Advancing Energy Justice in Alaska

NREL works with remote and rural communities across Alaska and the world to develop energy and building technologies that enhance culture, strengthen economies, and improve resilience in a changing environment. Alaska and Arctic communities not only face one of the harshest climates on earth; they also pay some of the highest energy and housing costs in the nation. NREL's Alaska Campus collaborates with communities and tribes to develop affordable, culturally appropriate homes and buildings that combine traditional knowledge and twenty-first century technology. Demonstration homes deployed all over Alaska prove out new technologies in energy efficiency, foundations, indoor air quality, and clean energy. Local workforce development is incorporated into every project: new homes are built by local workers, often including the home's future occupant. With the Arctic warming twice as fast as the rest of the planet, coastal erosion and thawing permafrost are wreaking havoc on roads, buildings, and infrastructure across Alaska. With an inclusive approach to R&D, NREL is helping communities adapt to a changing environment and advancing energy justice in the world's extremes.

Alaska housing prototypes↗
DOE OSTI2022

Extracting energy from ocean thermal and salinity gradients to power unmanned underwater vehicles: State of the art, current limitations, and future outlook

Thermal gradient energy-generation technologies for powering unmanned underwater vehicles (UUVs) or autonomous sensing systems in the ocean are mainly in the research development phase or commercially available at a limited scale, and salinity-gradient energy-generation technologies have not been adequately researched yet. The demand for self-powered UUVs suitable for long-term deployments has been growing, and further research related to small-scale ocean gradient energy systems is needed. In this study, we conducted a comprehensive review about harvesting energy from ocean thermal or salinity gradients for powering UUVs, focusing on gliders and profiling floats. Thermal gradient energy systems for UUVs based on phase change materials (PCM) cannot provide the energy required for powering autonomous sensing systems because of the systems' low energy conversion efficiency. Besides reducing energy consumption by developing more efficient electrical-mechanical systems, enhancing the thermal conductivity of the PCMs may help address this challenge by increasing the power generation rate of the UUVs. Several other emerging technologies, such as thermoelectric generators, shape memory alloys, and small-scale thermodynamic cycle systems, have shown potential for powering UUVs, but they are still only at the laboratory testing or conceptual design phase. The most advanced power generation technologies based on salinity gradients, reverse electrodialysis and pressure-retarded osmosis, are still not economically viable for large-scale deployment, mainly because of the high cost of the components required to operate in harsh saline environments. Our feasibility evaluation showed that existing salinity gradient power generation technologies are not directly feasible for powering UUVs in the open ocean.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Planning decentralized urban renewable energy systems using algal cultivation for closed-loop and resilient communities

To tackle climate challenges, communities need to harvest renewable energy and resources on site locally to close the loops for enhancing the resilience of communities facing unpredictable and uncertain future changes. A decentralization planning of urban renewable energy systems is proposed by treating urban waste streams and producing biomass through applying algal biotechnology. When applying algal technology as a renewable and decentralized energy source in urban systems, the overall performance can vary by levels of urban nutrients, solar and CO 2 resources, and the transportation cost when considering its application to different urban densities, urban form, and the spatial scale of urban settings. This research explores three potential impacts on the algal system’s energy performance: (1) urban density, (2) urban form in different contexts, and (3) spatial scale. The research examines the impacts by testing urban settings given in actual contexts in Atlanta, Georgia, USA. Four neighborhoods representing the high-density urban, mid-density urban, mixed suburban, and typical suburban areas are investigated. The density-scale–performance relationships are explored through testing different urban forms of neighborhoods in both hypothetical and actual neighborhood settings. A GIS-based model is developed to estimate the overall energy performance of the decentralized renewable energy system in urban environments. Results show that the energy performance is positive mainly for high-density urban neighborhoods with small-to-medium scales, up to 0.36 MJ per ton of municipal solid wastes for actual settings and 0.37 MJ for hypothetical cases. Neighborhoods with higher density have higher energy performance while up scaling has negative effects on the energy performance with a low degree of significance. Optimal scales are found as a 1-km radius in real test beds and 1.3 km in hypothetical settings, in which the results show trade-offs between scaling effects in the system efficiency gain and the transportation cost increase.

Quan, Steven Jige↗
DOE OSTI2022

Improving Abundance Estimates of Spring–Summer Snake River Chinook Salmon for Fisheries Management

Abstract The Columbia River basin is home to a run of spring–summer Chinook Salmon Oncorhynchus tshawytscha that returns to the Snake River drainage of Idaho, Oregon, and Washington in the Pacific Northwest. Historically, the run was one of the more productive throughout the Columbia River basin. However, Snake River spring–summer Chinook Salmon have experienced declines in abundance due to overfishing, habitat degradation, and dams. Several stocks are listed as threatened under the U.S. Endangered Species Act and are supported by mitigation hatcheries funded by Idaho Power Company, the Lower Snake River Compensation Plan, and the Bonneville Power Administration. To maximize tribal and state harvest of returning hatchery adults, minimize impacts on wild fish, and ensure that enough hatchery fish return to meet broodstock needs, careful fisheries management is required. Since 2008, managers have used hatchery adults, PIT-tagged as juveniles and detected at Lower Granite Dam, to generate adult abundance estimates. In season, these estimates inform state and tribal harvest shares and ensure that broodstock needs are met. Postseason, they provide smolt-to-adult survival and return rates. Since 2012, parentage-based tagging (PBT) has provided an alternative method to estimate stock- and age-specific returns at Lower Granite Dam, since returning hatchery adults sampled at Lower Granite Dam can be assigned to their parents. We compared stock-specific abundance estimates between PIT- and PBT-derived methodologies for return years 2016–2019. Across all years, PIT tag estimates accounted for 65% of the PBT-based estimates at Lower Granite Dam across all age-groups and release sites combined. This underrepresentation across all groups equated to 49,833 fish that were not accounted for in PIT tag abundance estimates. It is clear that PBT-based estimates should aide in-season harvest management and postseason run reconstruction to avoid the known bias of estimates from PIT tags, especially during years of low returns when increased accuracy is critical.

Coykendall, D. Katharine (ORCID:0000000211482397)↗
DOE OSTI2020

Simulating the Trajectory and Biomass Growth of Free-Floating Macroalgal Cultivation Platforms along the U.S. West Coast

Trajectory tracking and macroalgal growth models were coupled to support a novel macroalgae-harvesting concept known as the Nautical Off-shore Macroalgal Autonomous Device (NOMAD). The NOMAD consists of 5 km long carbon-fiber longlines that are seeded and free float southward along the U.S. West Coast for approximately 3 months before harvesting off the California coast, taking advantage of favorable environmental conditions. The trajectory and macroalgal growth models were applied to answer planning questions pertinent to the techno-economic analysis such as identifying the preferred release location, approximate pathway, timing until harvest, and estimated growth. Trajectories were determined with the General NOAA Operational Modeling Environment (GNOME) model, using 11 years of current and wind data, determining probabilities by running nearly 40,000 Monte Carlo simulations varying the start time and location. An accompanying macroalgal growth model was used to estimate the growth of macroalgae based on the trajectory tracks and environmental forcing products, including light, temperature and nutrients. Model results show that NOMAD lines transit south in the months of April to September due to seasonal currents, taking approximately 3 months to reach Southern California. During transit, NOMAD lines are dispersed but typically avoid beaching or passing through marine sanctuaries. NOMAD lines can yield up to 30 kg wet weight per meter of cultivation line.

09 BIOMASS FUELS↗
DOE OSTI2022

Harvesting krypton isotopes from the off-gas of an irradiated water target to generate 76Br and 77Br

Abstract A flowing-water target was irradiated with a 150 MeV/nucleon beam of 78 Kr at the National Superconducting Cyclotron Laboratory to produce 77 Kr and 76 Kr. Real-time gamma-imaging measurements revealed the mass transport of the krypton radioisotopes through the target-water processing, or “isotope harvesting”, system. The production rates were determined to be 2.7(1) × 10 –4 nuclei of 76 Kr and 1.18(6) × 10 –2 nuclei of 77 Kr formed per incident 78 Kr ion. Utilizing an off-gas processing line as part of the isotope harvesting system, a total of 7.2(1) MBq of 76 Kr and 19.1(6) MBq of 77 Kr were collected in cold traps. Through the decay, the daughter radionuclides 76 Br and 77 Br were generated and removed from the traps with an average efficiency of 77 ± 12%. Due to the differences in half-lives of 76 Kr and 77 Kr, it was possible to isolate a pure sample of 76 Br with 99.9% radionuclidic purity. The successful collection of krypton radioisotopes to generate 76 Br and 77 Br demonstrates the feasibility of gas-phase isotope harvesting from irradiated accelerator cooling-water. Larger-scale collections are planned for collecting by-product radionuclides from the Facility for Rare Isotope Beams.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Investigating a Renewable-Resource-Targeting Mobile Aquaculture System Using Route Optimization Based on Optimal Foraging Theory

Aquaculture systems require careful consideration of location, which determines water conditions, pollution impacts, and hazardous conditions. Mobility may be able to address these factors while also supporting the targeting of renewable energy sources such as wind, wave, and solar power throughout the year. In this paper, a purpose-built mobile aquaculture ship is identified and modeled with a combination of renewable energy harvesting capabilities as a case study with the objective of assessing the potential benefits of targeting high renewable energy potentials to power aquaculture operations. A route optimization algorithm is created and tuned to simulate the mobility of the aquaculture platform and cost-basis comparisons are made to a stationary system. The small spatial variability in renewable energy potential when combining multiple resources significantly limits the benefits of a mobile, renewable-targeting aquaculture system. On the other hand, the consistent energy harvest from a blend of renewable energy types (13 kW installed wind capacity, 661 m 2 installed solar, and 1 m characteristic width wave-energy converter) suggests that the potential benefits of a mobile platform for offshore aquaculture (mitigation of environmental and social concerns, any potential positive impact on yields, hazard avoidance, etc.) can likely be pursued without significant increases in energy harvester costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Feedstock-Conversion Interface Consortium Annual Review of Research – FY 2021

Led by the U.S. Department of Energy’s Bioenergy Technologies Office, the Feedstock-Conversion Interface Consortium (FCIC) is a multi-lab collaboration that develops first-principles-based knowledge and tools to understand, quantify, and mitigate the effects of feedstock and process variability across the bioenergy value chain, from the field and forest through downstream conversion. The FCIC recently published its Annual Review of Research Fiscal Year (FY) 2021 report, which presents an overview of the research accomplishments of FCIC members in FY 2021, covering both the low- and high-temperature conversion pathways and all three process areas: Feedstock harvest and storage Preprocessing Conversion. The key research achievements of each task are presented, along with planned FY 2022 work that will build on the FY 2021 accomplishments.

09 BIOMASS FUELS↗
DOE OSTI2024

Agent-based modeling to evaluate the effects of harvesting biomass and hunting on ring-necked pheasant ( Phasianus colchicus ) populations

As the US strives to divest from fossil fuels, dedicated energy crops have increased in popularity because of their potential to serve as a renewable source of transportation fuel and biomass-generated electricity. Whereas the impacts of crop agriculture on biodiversity have been well-documented, less is known about the potential impacts that growing bioenergy crops could have on wildlife. Here we look specifically at the ring-necked pheasant (Phasianus colchicus) and how pheasant populations may be influenced by growing and harvesting bioenergy crops. We explored effects of temporal harvest strategies at the field scale on biomass yield and pheasant population size. To investigate, we developed an agent-based model (ABM) that simulates ring-necked pheasants, tractors, hunters, and vegetation classes. One such vegetation class is land that is enrolled in a Conservation Reserve Program (CRP) plan specifically targeting pheasant conservation. Using this ABM, we assessed four different landscapes—corn-dominated, CRP-dominated, grassland-dominated, and mixed landscape—under strategies that varied the time of harvest. We also used ecological valuation to compare scenario outcomes from an economic perspective. We determined that biomass yield and pheasant population size were sensitive to harvesting times. Our scenarios totaled between ~$931,000 (minimum) and $3.8 million (maximum) over all landscapes, though on average harvesting in late spring generated the most value ($2.45 million) and harvesting in fall was a close second in terms of profitability ($2.39 million).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Improving sustainable tropical forest management with voluntary carbon markets

Due to a rapidly changing climate, voluntary carbon markets are gaining momentum and should be leveraged to improve and expand tropical sustainable forest management plans, limiting carbon emissions and enhancing critical carbon sinks. By sequestering more carbon than any other terrestrial ecosystem — ~1 Pg C yr –1 — tropical forests provide crucial natural climate solutions and opportunities in the evolving voluntary carbon market. Here, we argue that some issues with the current sustainable management of tropical forests can be addressed using carbon-focused sustainable forest management (SFM + C) to leverage financial resources for tropical forest carbon storage and sequestration. We suggest an extended harvest cycle in SFM + C and calculate an associated potential increase in aboveground carbon stocks of commercial timber of 1.26 Mg C ha –1 after each cycle in the Brazilian Amazon. The additional carbon storage due to a longer harvest cycle can generate carbon credits worth 152.6 (SD 9.2) US dollars per hectare in 40 years. Considering an average cost of 180 BRL per m3 of commercial timber delivered to the sawmill, an SFM + C plan with a 40-year cycle could generate 28.7% (SD 2.5) more profit than 35-year cycles by combining timber and carbon revenues. A robust carbon price could incentivize the further extension of harvest cycles, providing a monetary return that offsets the opportunity cost intrinsic to harvesting under longer cycles. Lastly, we highlight research needs to support tropical SFM + C, which can be part of a global collective effort to limit global warming to below 2 °C above pre-industrial levels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Regionalized Life Cycle Greenhouse Gas Emissions of Forest Biomass Use for Electricity Generation in the United States

This study presents a cradle-to-grave life cycle analysis (LCA) of the greenhouse gas (GHG) emissions of the electricity generated from forest biomass in different regions of the United States (U.S.), taking into consideration regional variations in biomass availabilities and logistics. The regional biomass supply for a 20 MW bioelectricity facility is estimated using the Land Use and Resource Allocation (LURA) model. Results from LURA and data on regional forest management, harvesting, and processing are incorporated into the GHGs, Regulated Emissions, and Energy Use in Technologies (GREET) model for LCA. The results suggest that GHG emissions of mill residues-based pathways can be 15-52% lower than those of pulpwood-based pathways, with logging residues falling in between. Nonetheless, our analysis suggests that screening bioenergy projects on specific feedstock types alone is not sufficient because GHG emissions of a pulpwood-based pathway in one state can be lower than those of a mill residue-based pathway in another state. Furthermore, the available biomass supply often consists of several woody feedstocks, and its composition is region-dependent. Forest biomass-derived electricity is associated with 86-93% lower life-cycle GHG emissions than the emissions of the average grid electricity in the U.S. Key factors driving bioelectricity GHG emissions include electricity generation efficiency, transportation distance, and energy use for biomass harvesting and processing.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Nutrient Replenishment by Turbulent Mixing in Suspended Macroalgal Farms

Abstract This study uses large eddy simulations to investigate nutrient transport and uptake in suspended macroalgal farms. Various farm configurations and oceanic forcing conditions are examined, with the farm base located near the nutricline depth. We introduce the Damkohler number Da to quantify the balance between nutrient consumption by macroalgae uptake and supply by farm‐enhanced nutrient transport. Most cases exhibit low Da , indicating that farm‐generated turbulence drives sufficient upward nutrient fluxes, supporting macroalgae growth. High Da and starvation may occur in fully grown farm blocks, a configuration that generates the weakest turbulence, particularly when combined with densely planted macroalgae or weak flow conditions. Flow stagnation within the farm due to macroalgae drag may constrain the uptake efficiency and further increase the starvation risk. Mitigation strategies involve timely harvesting, avoiding dense macroalgae canopies, and selecting farm locations with robust ocean currents and waves. This study provides insights for sustainable macroalgal farm planning.

Geology↗