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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Cannabis monoterpene synthases: evaluating structure–function relationships

Terpene synthases catalyze the first committed step in the biosynthesis of terpenes, a structurally diverse class of natural products that also encompasses volatiles derived from precursors in the C10 to C15 range (termed monoterpenes and sesquiterpenes, respectively). In the review section of this article, we are providing information about all functionally characterized monoterpene synthases (MTSs) and sesquiterpene synthases (STSs) of Cannabis sativa L. We are also exploring the locations of MTSs and STSs in the chromosome-level assembly of the reference chemovar CBDRx. A follow-up computational structure–function analysis focuses on MTSs, as there is already a rich literature available on the topic. More specifically, by employing sequence comparisons and homology structural modeling, we infer which amino acid residues are likely to constrain the available space in the active site of cannabis MTSs. The emphasis of these studies was to investigate why some MTSs accept only a C10 diphosphate as substrate, while mixed MTS/STS enzymes also accommodate a C15 diphosphate. Here, by combining a literature review and computational analyses in a hybrid format, we are laying the foundation for future studies to better understand the determinants of substrate and product specificity in these fascinating enzymes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Synthetic microbial communities: Bridging research and application in second-generation bioenergy feedstock microbiomes

The sustainable production of purpose-grown bioenergy feedstocks is essential in transitioning away from fossil fuels. Synthetic communities (SynComs) are consortia of microorganisms that can be used as biological interventions to support objectives like plant growth and stress tolerance. This review examines the state of knowledge regarding microbiomes and SynComs of second-generation bioenergy feedstocks, focusing on the rhizosphere. We first provide an overview of second-generation feedstocks, including switchgrass (Panicum virgatum), miscanthus (Miscanthus × giganteus), sorghum (Sorghum spp.), sugarcane (Saccharum spp.), and poplar (Populus spp.), and summarize our current understanding of their plant-soil-microbiome ecology. We next discuss considerations in the objectives, design, and evaluation of SynComs to enhance feedstock production, and then critically review the literature around their use. Our literature analysis revealed that SynCom performance varied substantially between controlled pilot experiments and field trials, possibly due to system complexity that could not be fully considered in their design and pilot evaluation. We identified a gap in the use of SynComs to support the unique sustainability objectives of biofuel feedstock agriculture, presenting an opportunity to leverage these additional microbial traits in SynCom designs. Finally, we emphasize the importance of targeted research to identify the ecological principles that govern the assembly, activation, and persistence of microbes in the feedstock rhizosphere, thereby enhancing our capacity to manage microbiomes under diverse environmental conditions and ensure their functionality. Beyond biofuels, SynComs are a promising microbiome management strategy for crop production; however, an ecologically informed design and evaluation of SynComs are advised.

SynCom↗
DOE OSTI2022

CLARIFYING THE NEXUS BETWEEN LIFE CYCLE ASSESSMENT AND CIRCULARITY INDICATORS: A SETAC/ACLCA INTEREST GROUP

Purpose Improving the circularity of resources is important to the sustainability of consumer goods. Current research has indicated that circularity practices and circular economy (CE) methods do not always reduce environmental impacts. The aim of this research is to investigate the adoption of the life cycle assessment (LCA) methodology to improve the environmental impacts of circularity practices. Methods As part of the Society for Environmental Toxicology And Chemistry (SETAC) forum, an interest group (IG) on Circularity and LCA was formed in partnership with the American Center for Life Cycle Assessment (ACLCA) to tackle methodological and technical issues related to circularity in LCA. The IG’s research approach is summarized in four key steps: defining goals and objectives, literature review and gap analysis, ideation, and experimentation. The twelve active persons within this IG meet monthly and have been divided into four sub-working groups (sub-WGs) so that complementary tasks can be completed concurrently in an effective manner. Each sub-WG meets monthly and reports back to the main group for collaboration and brainstorming to meet the research objectives. Results and discussion First, the sub-WG #1, focusing on the “pool of circularity and LCA-based indicators”, analyzed the complementarity between two of the most used circularity indicators and LCA. Second, the sub-WG #2, working on the “evaluation of CE loops performance through LCA”, built a mind map of pain points that reflect the challenges that the LCA practitioners face when combining LCA with CE approaches. Third, the sub-WG #3, dealing with the “trade-offs between circularity and sustainability”, highlighted key alignments and/or conflicts between circularity and sustainability performance depending on the scope, product, industry, or system of analysis. Fourth, the sub-WG #4, focusing on “business and industrial cases”, plans to leverage the knowledge base developed within this IG to develop use cases documenting the benefits and challenges associated with CE-related loops modeling in LCA. Conclusions The first findings of this SETAC/ACLCA IG provide a state-of-the-art overview of the synergists of LCA methodology and the CE measurement frameworks reported in the literature. To move forward and capitalize on the first findings, one valuable point will be to discuss and provide concrete solutions to the pain points that emerged when considering circularity in LCA. Eventually, the knowledge base and resources created within this IG ultimately aim to support the proper application of LCA for practitioners in CE contexts, and could provide relevant inputs for the ISO Technical Committee ISO/TC 323 working on the upcoming standard for the measurement of CE performance.

Life cycle assessment, circular economy, circulari↗
DOE OSTI2025

A review on bacteria-derived antioxidant metabolites: their production, purification, characterization, potential applications, and limitations

Abstract Antioxidants are organic molecules that scavenge reactive oxygen species (ROS) and reactive nitrogen species (RNS), thereby maintaining cellular redox balance in living organisms. The human body synthesizes endogenous antioxidants, whereas humans obtain exogenous antioxidants from other organisms such as plants, animals, fungi, and bacteria. This review primarily focuses on the antioxidant potential of natural metabolites and extracts from five major bacterial phyla, including the well-studiedActinobacteriaandCyanobacteria, as well as less-studiedBacteroides,Firmicutes, andProteobacteria.The literature survey revealed that the metabolites and the extracts with antioxidant activity can be obtained from bacterial cells and their culture supernatants. The metabolites with antioxidant activity include pigments, phycobiliproteins, polysaccharides, mycosporins-like amino acids, peptides, phenolic compounds, and alkaloids. Both metabolites and extracts demonstrate in vitro antioxidant capacity through radical-scavenging, metal-reducing, and metal-chelating activity assays. In in vivo models, they can scavenge ROS and RNS directly and/or indirectly eliminate them by enhancing the activities of antioxidant enzymes, such as catalase, superoxide dismutase, and glutathione peroxidase. Due to their antioxidant activities, they may find applications in the cosmetic industry as anti-aging agents for the skin and in medicine as drugs or supplements for combating oxidative stress-related disorders, such as neurodegenerative diseases and diabetes. The literature survey also elucidated that some metabolites and extracts with antioxidant activity also exhibited strong antimicrobial properties. Therefore, we consider that they may have future applications in the treatment of infectious diseases, the preparation of pathogen-free healthy foods, and the extension of food shelf life.

Pharmacology & Pharmacy↗
DOE OSTI2025

Electricity Markets and Long-Duration Energy Storage: A Survey of Grid Services and Revenue Streams

Purpose of Review Long Duration Energy Storage (LDES) is increasingly viewed as a potential resource for providing grid services that enhance the stability and flexibility of electricity systems. While some LDES services are integrated into existing market frameworks, traditional mechanisms may not fully account for their operational characteristics, potentially leading to undervaluation. Within this context, this paper reviews the literature and industry practices to assess potential grid services for LDES, evaluates existing compensation mechanisms, and identifies challenges to full market integration. Recent Findings We first review existing literature and identify key grid services unique to LDES, including enhancing grid resilience during extreme weather events, enabling long-term energy shifting, and providing flexible and firm energy in systems with limited dispatchable resources. Here, we also review how LDES services are compensated in current market frameworks and the challenges associated with the full realization of LDES values. Additionally, we summarize market mechanisms for storage technologies across U.S. wholesale markets. We find that some markets are adjusting incentive structures, such as incorporating storage duration in capacity accreditation, to better align with system needs and LDES contributions to the grid. However, further refinements in capacity remuneration and dispatch timeframes may be needed for more effective realization of LDES value. Summary This review evaluates potential grid services for LDES, examines existing compensation mechanisms for LDES technologies, and identifies gaps between these mechanisms and LDES operational characteristics. The review concludes by outlining potential market enhancements for more effective LDES integration and articulating additional research needs to support its efficient participation in future power systems.

Flexible resources↗
DOE OSTI2025

A Review of Abrupt Permafrost Thaw: Definitions, Usage, and a Proposed Conceptual Framework

Purpose of ReviewWe review how ‘abrupt thaw’ has been used in published studies, compare these definitions to abrupt processes in other Earth science disciplines, and provide a definitive framework for how abrupt thaw should be used in the context of permafrost science.Recent FindingsWe address several aspects of permafrost systems necessary for abrupt thaw to occur and propose a framework for classifying permafrost processes as abrupt thaw in the future. Based on a literature review and our collective expertise, we propose that abrupt thaw refers to thaw processes that lead to a substantial persistent environmental change within a few decades. Abrupt thaw typically occurs in ice-rich permafrost but may be initiated in ice-poor permafrost by external factors such as hydrologic change (i.e., increased streamflow, soil moisture fluctuations, altered groundwater recharge) or wildfire.SummaryPermafrost thaw alters greenhouse gas emissions, soil and vegetation properties, and hydrologic flow, threatening infrastructure and the cultures and livelihoods of northern communities. The term ‘abrupt thaw’ has emerged in scientific discourse over the past two decades to differentiate processes that rapidly impact large depths of permafrost, such as thermokarst, from more gradual, top-down thaw processes that impact centimeters of near-surface permafrost over years to decades. However, there has been no formal definition for abrupt thaw and its use in the scientific literature has varied considerably. Our standardized definition of abrupt thaw offers a path forward to better understand drivers and patterns of abrupt thaw and its consequences for global greenhouse gas budgets, impacts to infrastructure and land-use, and Arctic policy- and decision-making.

Webb, Hailey↗
DOE OSTI2022

Investigation of Remanufacturing Methods Utilizing Mechanically-Generated and Gas-Atomized Feedstock Production for Directed Energy Deposition Processing Through Specific Energy Consumption Modeling

The goal of this work was to assess the energy consumption of a novel remanufacturing method that utilizes mechanically-generated feedstock for directed energy deposition. Gas-atomization is the state-of-the-art production process for directed energy deposition feedstock, and for this work was incorporated into a remanufacturing process path to provide a comparison method. Specific energy consumption models of remanufacturing utilizing both of these proposed process paths were developed and applied to a case study to investigate energy saving opportunities for future manufacturing paradigms. The energy modelling was done in three stages. First, the mechanically-generated feedstock production energy consumption model was generated from experimental observation. Second, the gas-atomized feedstock production energy consumption model was generated from a combination of experimental observation, reported estimates from the manufacturer, and data found in the literature. Lastly, the energy consumption model of directed energy deposition was derived from experimental observation and compares favorably with reported estimates in the literature. With the models, the specific energy consumption in the two process paths were compared and their application was demonstrated by estimating the energy consumption to remanufacture a bracket. The two feedstock production methods had similar specific energy consumptions. The specific energy consumption of the directed energy deposition process was the greatest component in the respective remanufacturing paths by an order of magnitude; increasing deposition rate is the most important factor for lowering the overall specific energy consumption. The analyzed remanufacturing technologies were estimated to consume less energy than replacement when repairing up to approximately 15% of the original part’s mass.

42 ENGINEERING↗
DOE OSTI2020

Chapter 3 - Harvesting Thermal Energy with Ferroelectric Materials

Thermal energy is considered the ubiquitous form of energy as all other forms of energy ultimately degrade to thermal energy. Thermal to electrical energy conversion is currently an important method for electricity generation. The traditional power cycles, however, become technically and economically unviable when heat source temperature reduces below 100 °C. Thermal energy harvesting has been proposed as a viable alternative for low-grade thermal to electrical energy conversion. Thermal energy harvesting is a method of generating electricity using temperature-induced variations in material properties. It is particularly useful when the heat sources are available free-of-cost, either in the form of natural heat or waste heat. While there are several thermal energy harvesting techniques, pyroelectricity is the most promising as it can be used in situations where spatial thermal gradients are difficult to establish or the temperature of the heat source is changing. Despite the fact that the pyroelectric effect in ferroelectric materials has been known for past several decades, thermal energy harvesting using ferroelectric materials has not been much explored in the literature. In this chapter, we would explore various aspects of thermal energy harvesting using ferroelectric materials. We have first established the physics behind ferroelectricity, then provided a detailed description on ferroelectric thermal energy harvesting, discussed various ferroelectric thermodynamic cycles and devices proposed in the literature, and finally described various other emerging applications of ferroelectricity.

DIRECT ENERGY CONVERSION,MATERIALS SCIENCE,OTHER I↗
DOE OSTI2021

Sensor impacts on building and HVAC controls: A critical review for building energy performance

Building operations rely heavily on control systems and sensors. This paper provides a sophisticated literature review on sensor systems in building/HVAC systems, particularly in the context of controls, and their impacts on energy efficiency and thermal comfort. This study aims to understand the previous and current research and identify future research opportunities on this subject. The reviewed sensor systems were used for heuristic rule-based controls, local controls, and advanced optimal controls for existing and emerging technologies. Five major aspects of sensors are reviewed here: control loops for sensors, sensor types, sensor locations, sensor data, and a sensor impact evaluation framework. To augment the literature review, comprehensive standardized interviews were also conducted with relevant industry experts and practitioners. These interviews were designed and performed to (1) identify significant factors for selecting sensor sets and current undergoing issues, (2) identify potential improvements in sensor configuration/deployment, and (3) integrate expert (e.g., researcher, building operation practitioner) knowledge and experiences to develop structured use-case scenarios. Researchers collected and analyzed 31 interview responses for this paper.

Building control↗
DOE OSTI2021

A review of machine learning in building load prediction

The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.

Liang, Zhang↗
DOE OSTI2021

The nth-plant scenario for blended feedstock conversion and preprocessing nationwide: biorefineries and depots

The sustainability of the biofuel industry depends on the development of a mature conversion technology on a national level that can take advantage of the economies of scale: the nth-plant. Defining the future location and supply logistics of conversion plants is imperative to ultimately transform the nation’s renewable biomass resources into cost-competitive, high-performance feedstock for production of biofuels and bioproducts. Since the US has put restrictions on production levels of conventional biofuels from edible resources, the nation needs to plan for the widespread accessibility and development of the cellulosic biofuel scenario. Conventional feedstock supply systems will be unable to handle cellulosic biomass nationwide, making it essential to expand the industry with an advanced feedstock supply system incorporating a distributed network of preprocessing depots and conversion plants, or biorefineries. Current studies are mostly limited to designing supply systems for specific regions of the country. We developed a national database with potential locations for depots and biorefineries to meet the nation’s target demand of cellulosic biofuel. Blended feedstock with switchgrass and corn stover (harvested by either a two- or three-pass method) are considered in a Mixed Integer Linear Programming model to deliver on-spec biomass that considers both, a desired quantity and quality at the biorefinery. A total delivered feedstock cost that is less than $79.07/dt (2016$) is evaluated for years 2022, 2030, and 2040. In 2022, 124 depots and 59 biorefineries could be supplied with 42.8 million dt of corn stover and switchgrass. In 2030 and 2040, the total accessible biomass could increase to 215% and 393% respectively when compared to 2022. However, an $8/dry tons reduction in targeted delivery cost could reduce total accessible biomass by 67%. Kansas, Nebraska, South Dakota and Texas were identified as potential states with a strong biofuel economy given that they had six or more biorefineries located in all scenarios. In some scenarios, Colorado, Alabama, Georgia, Minnesota, Mississippi and South Carolina would greatly benefit from a depot network as these could only deliver to a biorefinery in a nearby state. To elaborate the impact of a nationwide consideration, the findings were compared with existing literature for different US regions. We also present results for biorefinery capacities that are double, triple, and quadruple in size.

09 BIOMASS FUELS↗
DOE OSTI2022

Sizing battery energy storage and PV system in an extreme fast charging station considering uncertainties and battery degradation

In this paper, we present mixed integer linear programming (MILP) formulations to obtain optimal sizing for a battery energy storage system (BESS) and solar generation system in an extreme fast charging station (XFCS) to reduce the annualized total cost. The proposed model characterizes a typical year with eight representative scenarios and obtains the optimal energy management for the station and BESS operation to exploit the energy arbitrage for each scenario. Contrasting extant literature, this paper proposes a constant power constant voltage (CPCV) based improved probabilistic approach to model the XFCS charging demand for weekdays and weekends. This paper also accounts for the monthly and annual demand charges based on realistic utility tariffs. Furthermore, BESS life degradation is considered in the model to ensure no replacement is needed during the considered planning horizon. Different from the literature, this paper offers pragmatic MILP formulations to tally BESS charge/discharge cycles using the cumulative charge/discharge energy concept. McCormick relaxations and the Big-M method are utilized to relax the bi-linear terms in the BESS operational constraints. Finally, a robust optimization-based MILP model is proposed and leveraged to account for uncertainties in electricity price, solar generation, and XFCS demand. Case studies were performed to signify the efficacy of the proposed formulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems

Long-term operation of heating, ventilation, and air conditioning (HVAC) systems will eventually lead to a range of HVAC system failures, resulting in excessive energy consumption and maintenance costs. Here, to avoid HVAC malfunctioning, fault detection diagnostic (FDD) is utilized as a common practice. Machine learning methods have lately received considerable interest for FDD analysis of HVAC systems due to their high detection accuracy. Meanwhile, HVAC malfunctions are regarded as rare occurrences, hence normal operating data samples are much more accessible than data samples in faulty and malfunctioning conditions. The dominating frequency of normal operation in HVAC datasets has also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing the accuracy of the models which leads to a high number of false positives (misleading alarms) in the system. In order to enhance the performance of diagnostic procedures and fill the mentioned gaps, this study proposes a novel data-driven framework. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. By proposing this framework, three persistent challenges are addressed: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Beyond Price Taker: Conceptual Design and Optimization of Integrated Energy Systems Using Machine Learning Market Surrogates

Future electricity generation systems must be optimized to provide flexibility that counteracts the variability of non-dispatchable renewable energy sources and ensures the reliability and safety of critical infrastructure, including the electric grid. The current state-of-the-art is to co-optimize the design and operation of integrated energy systems (IES) treating historical or predicted time-series electricity prices as fixed parameters. Recent literature has shown the limitations of this price taker assumption, which neglects how IES optimization decisions influence market outcomes. As such, this paper proposes a new optimization formulation that uses machine learning surrogate models, trained from a library of annual market operation simulations, to embed IES market interactions into the co-optimization problem directly. Using a thermal generator example built in the open-source IDAES computational environment, we show that the price taker approach routinely over-predicts annual revenues by 8% or more compared to a validation simulation, where the proposed approach has a typical relative error of 1% or less.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Flexible dynamic boundary microgrid operation considering network and load unbalances

Flexible microgrids with dynamic boundaries have recently been introduced in the literature. With the ability to reconfigure the topology of the microgrids dynamically through remotely controlled switches, flexible microgrids with dynamic boundaries can further improve the resiliency and energy efficiency of microgrids with distributed energy resources (DERs). This paper focuses on the optimal operation considering one of the predominant characteristics of microgrids and distribution systems – unbalanced networks and loads. In existing literature, balanced modeling of microgrids is more common due to its attractive simplicity. The three-phase power unbalance has not been considered as a constraint on the generation units in a microgrid. Further, negative sequence constraints have also been neglected. In this article, we propose a set of constraints that is specifically related to the capabilities of inverter interfaced resources to supply unbalanced current/power when the microgrid is islanded from the main distribution grid. We incorporate the new set of constraints into two optimization formulations leveraging two convex relaxations of the three-phase power flow equations: mixed-integer linear programming (MILP) and mixed-integer semidefinite programming (MISDP) that optimize the dispatch of controllable switches and DERs in the microgrid. The algorithms are then extended to networked microgrids with grid-forming sources. We test the algorithms on a realistic community microgrid model in Puerto Rico as well as standardized IEEE distribution test feeders. The testing results demonstrate the performance of the proposed algorithms. The MILP is fast and scalable, and the MISDP enforces the negative sequence voltage constraints.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Investigation of heat pump technologies for high-temperature applications above 250 °C

This paper examines heat pump (HP) technologies to achieve heat supply temperatures above 250 °C, referred to as ultra-high-temperature heat pump (UHTHP). UHTHP, as a low-carbon heat delivery and heat augmentation technology, can offer an alternative to traditional combustion heating for decarbonizing high-temperature industrial processes. However, the heat supply temperature of over 250 °C surpasses the temperature range typically covered in the high-temperature HP literature and existing capabilities of commercial HP systems. This paper reviews HP technologies with the potential to evolve into UHTHP. Additionally, UHTHP studies in the literature are analyzed to compare the pros, cons, and technical potentials of various HPs using different thermodynamic cycles and fluids. Furthermore, we then pinpoint the technical gaps and challenges that have hindered widespread adoption of UHTHP in industry, explore potential improvements and solutions, and discuss the feasibility. Current efforts aim to lay the ground for future research, development, and commercial deployment for UHTHP.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2023

A chemistry-informed hybrid machine learning approach to predict metal adsorption onto mineral surfaces

Historically, surface complexation model (SCM) constants and distribution coefficients (K d ) have been employed to quantify mineral-based retardation effects controlling the fate of metals in subsurface geologic systems. Our recent SCM development workflow, based on the Lawrence Livermore National Laboratory Surface Complexation/Ion Exchange (L-SCIE) database, illustrated a community FAIR data approach to SCM development by predicting uranium(VI)-quartz adsorption for a large number of literature-mined data. Here, we present an alternative hybrid machine learning (ML) approach that shows promise in achieving equivalent high-quality predictions compared to traditional surface complexation models. At its core, the hybrid random forest (RF) ML approach is motivated by the proliferation of incongruent SCMs in the literature that limit their applicability in reactive transport models. Our hybrid ML approach implements PHREEQC-based aqueous speciation calculations; values from these simulations are automatically used as input features for a random forest (RF) algorithm to quantify adsorption and avoid SCM modeling constraints entirely. Named the LLNL Speciation Updated Random Forest (L-SURF) model, this hybrid approach is shown to have applicability to U(VI) sorption cases driven by both ion-exchange and surface complexation, as is shown for quartz and montmorillonite cases. The approach can be applied to reactive transport modeling and may provide an alternative to the costly development of self-consistent SCM reaction databases.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

A novel 3-D pulsating heat pipe module for high heat-flux applications

This study proposed a novel pulsating heat pipes (PHP) for high-flux thermal module. According to the open literature, the maximum value of the heat flux is 23.7 W∙cm –2 handled by PHP, which is far behind industry requirements. Therefore, the present study proposes a novel 3D PHP suitable for high heat-flux applications. The PHP were experimentally tested for different working fluids, including methanol and water. Furthermore, the experiments were performed to analyze the influence of various operating parameters, such as filling ratio (25–60%), heat load (100–800 W), and air flow rate 100–250 cubic feet per minute (CFM). In this work, the results showed that this PHP can sustain a heat flux up to 70 W∙cm –2 with the maximum case temperature under 80 °C, which is approximately three times higher than the available literature. Furthermore, the performance of the PHP module was compared against the commercial module with wicked heat pipe. The PHP exhibited about 7% higher heat dissipation compared to the commercial module. Since the proposed module uses simple copper tubes in its construction over the wicked structure used in commercial module, the novel 3D PHP module provides additional economic and mass production capability.

3D pulsating heat pipe↗