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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Design optimization of MAPS-based detectors using a data-driven fast simulation approach

A parametric simulation tool for pixel sensors is presented. A realistic pixel response is simulated purely based on measurement input, without requiring detailed knowledge of the underlying manufacturing process. As such, it provides an efficient alternative to the use of Technology Computer-Aided Design simulations, which typically depend on proprietary process information. Due to its parametric approach, the package is fast and thus particularly useful for larger detector systems and high hit rate environments. This work presents measurements, simulation and its validation for the MALTA2 sensor. It is a small collection electrode monolithic active pixel sensor produced in the Tower 180 nm complementary metal-oxide-semiconductor imaging process. Modifications to the sensor’s periphery, mainly in the hit merger, are studied in order to optimize the performance for tracking and calorimetry. This optimization is of special interest as part of the MALTA3 sensor redesign in the 65 nm Tower Partners Semiconductor Co. process.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Choppy Lite

The existing methodologies for gathering and computing zonal statistics from the combination of a raster image and a shapefile either require advanced programming knowledge in at least one language or the use of proprietary software such as ArcGIS. The software “Choppy Lite” offers several solutions for acquiring zonal statistics that can be used without proprietary software or programming knowledge. This software provides a Docker-based ecosystem, allowing users of any experience level to simply run a single line command in a terminal prompt independent of the operating system. Intermediate to advanced users can import the class provided by the software package into their own Python scripts.

Grant, JoshuaN.↗
DOE OSTI2026

Subsurface mechanical damage of fused silica glass during grinding by various sub-aperture tools with and without ultrasonics

The subsurface mechanical damage (SSD) depth after grinding fused silica glass with a comprehensive set of sub-aperture fixed abrasive grinding tools [cup, wheel, belt, pad, and rotary face mill (with and without ultrasonics)] and process parameters has been statistically measured using the taper wedge technique and evaluated. Consistent with a previously reported grinding model [J. Non-Cryst. Solids 352, 5601–5617 (2006) Crossref , Materials Science and Technology of Optical Fabrication (Wiley & Sons, 2018)], based on the sliding indentation fracture by sliding particles or asperities where the normal load per particle determines the depth of the fracture (and ultimately the overall SSD depth), the dominant factor controlling SSD depth was found to be the abrasive size regardless of the tool type and process conditions. Compared to full aperture grinding methods, the overall SSD depth was higher using the sub-aperture tools, likely due to the higher effective pressure and higher load per particle distribution. Here, in addition to abrasive size, a significant reduction in SSD depth was achieved by: (1) reducing the load distribution on the abrasive particles via increase in contact area and/or decrease in mechanical loading; (2) using a more compliant host tool medium; and (3) in what we believe is a more novel way, using ultrasonics. Combining low abrasive size, larger contact area, and a compliant host, the 6 µm diamond in a resin matrix (Trizact) on a foam pad led to very low SSD depth (~ 4.6 µm), relatively fast grinding rate (186 mm 3 /h), and little or no figure degradation. This grinding tool/process is an attractive choice for final grind, resulting in significantly reduced polish out (i.e., “grey out”) times. With the rotary face mill tool, the use of ultrasonics consistently led to a SSD depth reduction (ranging from 17%–34%). A new fracture mechanics-based model, to the best of our knowledge, where the relevant normal load is parallel to the feed direction, has been developed to explain how ultrasonics leads to lower SSD depth. The key factors, supported by finite element stress analysis and load measurements, are (1) the initiation of fractures at higher z heights during the tool’s ultrasonic vertical oscillations, thus propagating less deep into workpiece; (2) reduction in load (and therefore reduction in fracture propagation distance) due to smaller tool-workpiece feed direction contact area (again caused by higher heights relative to depth of cut); (3) upward movement of the tool during oscillation leads to fracturing toward the surface instead into the depth; and finally (4) at tool’s lowest point of oscillation cycle, there may not be enough time for the fracture to propagate to its full length.

Optics and optical instruments↗
DOE OSTI2021

Advanced Syngas Cleanup Using Radically Engineered Modular Systems

The overall objective of this project was to address key knowledge gaps and develop modular sorbent-based warm syngas cleanup designs that will enable 1- to 5-MW REMS-based plants to be cost-competitive with large state-of-the-art commercial plants utilizing all of our abundant domestic coal reserves. We proposed to develop two potential new desulfurization process designs specifically targeted for REMS plants for combined heat and power or polygeneration from coal gasification. The first design is a modification of our commercially offered dual transport reactor design based Warm-gas Desulfurization Process (WDP) where the transport-reactor regenerator is replaced with a fluidized-bed regenerator. This design leverages the learnings obtained from our pre-commercial demonstration testing of the WDP technology for over 3,000 hours and conclusions from the lessons learned workshop. The second design is a fixed-bed process that leverages the successful composition and chemistry of our WDP sorbent to develop an effective fixed-bed sorbent formulation and fixed-bed process design. These two potential desulfurization process designs have a strong technical likelihood of success and offer the best opportunity to effectively employ design standardization, mass production and advanced manufacturing techniques to enable cost competitiveness for syngas cleanup at smaller scale.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Evaluation of Joint Cyber/Safety Risk in Nuclear Power Systems

This report presents an analysis of the Emergency Core Cooling System (ECCS) for a generic Boiling Water Reactor (BWR)-4 NPP. The Electric Power Research Institute (EPRI) developed Hazards and Consequences Analysis for Digital Systems (HAZCADS) process is applied to the ECCS and its subsystems to identify unsafe control actions (UCAs) which act as possible cyber events of concern. The analysis is performed for two design basis events: Small-break Loss of Coolant Accident (SLOCA) and general transients (TRANS), such as unintended reactor trip. In previous work, HAZCADS UCAs were combined with other cyber-attack analysis to develop a risk-informed approach; however, this was for a single system. This report explores advanced systems engineering modeling approaches to model the interactions between digital assets across multiple systems which may be targeted by cyber adversaries. The complex and interdependent design of digital systems has the potential to introduce emergent cyber properties that are generally not covered by hazard analyses nor formal nuclear Probabilistic Risk Assessment (PRA). The R&D and supporting analysis presented here explores approaches to predict and manage how interdependent system properties effect risk. To show the potential impact of a successful cyber-attack to formal PRA event tree probabilities, HAZCADS analysis was also used. HAZCADS was also used to model the automatic depressurization system (ADS) automatic actuation. This analysis extended to an integrated system analysis for common-cause failure (CCF). In this aspect, the HAZCADS analysis continued by analyzing plant design details for system connectivity in support of critical plant functions. A dependency matrix was developed to depict the integrated functionality of the interconnected systems. Areas of potential CCF are indicated. Future work could include adversary attack development to show how CCF could be caused, resulting in PRA events. Across the multiple systems that comprise the ECCS, the analysis shows that the change in such probabilities was very different between systems. This indicates that some systems have a larger potential risk impact from successful cyber-attack or digital failure, which indicates a need for these systems to have a higher priority for design and defensive measures. Furthermore, we were able to establish that a risk analysis using any arbitrary threat model establishes an ordering of components with regard to cyber-risk. This ordering can be used to influence the overall system design with an eye to lowering risk, or as a way to understand real-time risk to operational systems based on a current threat landscape. Expert knowledge of both the analysis process and the system being analyzed is required to perform a HAZCADS analysis. The need for a tiered risk analysis is demonstrated by the results of this report.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Workflow for Developing and Operating Subsurface Hydrogen Storage Facilities in Porous Reservoirs

Long-duration (seasonal) storage of natural gas (NG), which primarily consists of methane (CH 4 ), has been practiced for more than a hundred years at underground gas storage (UGS) facilities that use depleted hydrocarbon reservoirs, saline aquifers, and salt caverns. To enable hydrogen (H 2 ) to be used as a long-duration, energy-storage medium, similar facilities are envisioned for underground H 2 storage (UHS) of either H 2 or H 2 /NG mixtures. Experience with UGS can be used to guide recommended practices for developing and operating UHS facilities in porous reservoirs. The most important factors (formation/fluid properties and engineering choices) that influence the performance of UHS reservoirs have been identified and quantified in previous studies. These factors and choices influence phenomena that determine the sweep efficiency of the stored working gas. These phenomena include viscous fingering, hysteretic capillary trapping, and gravity override of the working gas, as well as the upconing of nonproductive fluid that determine the sweep efficiency of the stored working gas. This report describes initial recommended-practices and a project-development workflow for UHS facilities that utilize porous reservoirs, based on the current state-of-knowledge about H 2 behavior in the subsurface. The workflow sequentially addresses all aspects of UHS project development, including the identification of H 2 sources and users, site ranking and down-selection, geologic and reservoir-engineering characterization, reservoir design, testing, risk management, commissioning, operations, and monitoring for a UHS facility. The goal is to enable UHS facilities to be developed in an efficient and timely manner, while carefully managing project risks. This workflow is similar to that which has been developed for UGS facilities (see Figure 1 of API, 2022), with the addition of tasks and subtasks specific to H 2 and UHS. The project-development workflow is broken down into three major stages: (1) define the H 2 use case; (2) rank, down-select, and characterize potential, candidate UHS sites; and (3) reservoir design, integrity testing, risk assessment, commissioning, operations, and monitoring for selected UHS sites. Each major stage is further broken down into tasks and subtasks, which are described at a high level. This report also provides more detailed descriptions of all tasks and subtasks that involve reservoir analysis and testing.

08 HYDROGEN↗
DOE OSTI2023

Distinguishing photo-induced oxygen attack on alkyl chain versus conjugated backbone for alkylthienyl-benzodithiophene (BDTT)-based push–pull polymers

Synthetic design has enabled increasing power conversion efficiency advances in organic photovoltaics (OPV). One continuing knowledge gap is detailed understanding of single-material chemical (photo)stability. Many considerations are based on prior OPV-relevant donor homopolymer systems rather than the next-generation push–pull architectures. Generally, energetic offsets between the lowest occupied molecular orbital of the donor and molecular oxygen are assumed to dictate kinetics of photo-induced charge transfer to a super oxide radical. Herein we determine the ambient-induced photo-degradation pathways, presented as proposed site-specific arrow-pushing mechanisms, for five donor polymers all containing the same push unit – alkylthienyl-substituted-benzodithiophene (BDTT) – but with chemically distinct pull units. The donor-only polymer films were subject to controlled photobleaching in air as an accelerated degradation approach, coupled with simultaneous monitoring of absorptance. Sample subsets were periodically removed and analyzed with X-ray photoelectron spectroscopy, to evaluate near-surface chemical composition and oxygen additions to hetero reporter atoms on either the BDTT push unit or distinct pull unit (i.e., sulphur and nitrogen). Here, this methodology allows us to distinguish between different mechanisms of bond cleavage and formation. Overall, we find that neither polymer redox properties nor individual push or pull unit stability are sufficient to predict photo-oxidative degradation of these polymers. Rather, there is a greater dependence on the susceptibility of unique structural groups within a polymeric system. Alkyl chain oxygen addition is generally the first attack site and direct sulfur oxidation on the conjugated backbone occurs after saturation of the alkyl chain initiation sites. This work provides a standard that could be used to evaluate relative photo-oxidative (in)stability for new OPV materials quickly – prior to time-consuming device optimization – and demonstrates an effective methodology for correlating optical degradation with chemical structure alterations via spectroscopic signatures to guide synthetic design.

14 SOLAR ENERGY↗
DOE OSTI2022

Capture Instead of Release: Defect-Modulated Radionuclide Leaching Kinetics in Metal–Organic Frameworks

Comparison of defect-controlled leaching-kinetics modulation of metal–organic frameworks (MOFs) and porous functionalized silica-based materials was performed on the example of a radionuclide and radionuclide surrogate for the first time, revealing an unprecedented readsorption phenomenon. On a series of zirconium-based MOFs as model systems, we demonstrated the ability to capture and retain >99% of the transuranic 241 Am radionuclide after 1 week of storage. Here, we report the possibility of tailoring radionuclide release kinetics in MOFs through framework defects as a function of postsynthetically installed organic ligands including cation-chelating crown ether-based linkers. Based on comprehensive analysis using spectroscopy (EXAFS, UV–vis, FTIR, and NMR), X-ray crystallography (single crystal and powder), and theoretical calculations (nine kinetics models and structure simulations), we demonstrated the synergy of radionuclide integration methods, topological restrictions, postsynthetic scaffold modification, and defect engineering. This combination is inaccessible in any other material and highlights the advantages of using well-defined frameworks for gaining fundamental knowledge necessary for the advancement of actinide-based material development, providing a pathway for addressing upcoming challenges in the nuclear waste administration sector.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species

Abstract Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI’s ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Swirls and scoops: Ice base melt revealed by multibeam imagery of an Antarctic ice shelf

Knowledge gaps about how the ocean melts Antarctica’s ice shelves, borne from a lack of observations, lead to large uncertainties in sea level predictions. Using high-resolution maps of the underside of Dotson Ice Shelf, West Antarctica, we reveal the imprint that ice shelf basal melting leaves on the ice. Convection and intermittent warm water intrusions form widespread terraced features through slow melting in quiescent areas, while shear-driven turbulence rapidly melts smooth, eroded topographies in outflow areas, as well as enigmatic teardrop-shaped indentations that result from boundary-layer flow rotation. Full-thickness ice fractures, with bases modified by basal melting and convective processes, are observed throughout the area. This new wealth of processes, all active under a single ice shelf, must be considered to accurately predict future Antarctic ice shelf melt.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Effective Knowledge Dissemination for LMI Solar: The Roles of Community Organizations and State Governments

This report provides a detailed summary of a three-year effort by the Clean Energy States Alliance (CESA) on “Effective Knowledge Dissemination for LMI Solar: The Roles of Community Organizations and State Governments.” The core approach of this project was to work with state energy agencies (SEAs) and community-based organizations (CBOs) to improve their ability to share the knowledge and information that is needed for solar to be developed efficiently, equitably, and cost-effectively in low- and moderate-income (LMI) communities.

14 SOLAR ENERGY↗
DOE OSTI2022

Spatial Transferability of Machine Learning Based Volume Estimation Models

High-quality traffic volume data is essential for efficient transportation planning and operations. However, such high-quality data is expensive to collect, owing primarily to the high capital cost of installing and maintaining continuous counting stations (CCSs). Recent availability of probe-based vehicle data offers a cost-effective solution for increasing the observability of traffic volumes. However, having ample ground truth traffic data is a prerequisite for developing robust volume estimation models. Though this might not be a big issue in many states, states with scarce CCS data might be able to benefit from robust volume estimation models developed in (adjacent) data-rich states. While there is a reasonable amount of spatial transferability research in the transportation domain, there is a dearth of knowledge on the spatial transferability of probe-based volume estimation models. To address this gap, this paper explores spatial transferability of volume estimation models developed from data in three states (Colorado, North Carolina, and Pennsylvania). Results indicate that it is extremely important to maintain temporal consistency when attempting spatial transferability of volume estimation models. It was also found that models trained on regions with lower peak traffic volumes will limit the performance of models transferred to states with higher peak hourly traffic volumes. Corroborating findings from existing spatial transferability research on other topics, it was found that a meta-model (developed using data from multiple states) performs better than volume estimation models developed within any one of the states.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Restructuring and integrity of molecular catalysts in electrochemical CO 2 reduction

Electrocatalysts that start a reaction as molecules do not always end the reaction as molecules, and even when they do, they might not be molecules during catalysis. In this Perspective, we discuss knowledge learned from the study of Cu-based molecularly structured electrocatalysts––including metal coordination complexes, metal-organic frameworks, single-atom catalysts, and polymeric materials––that restructure under electrochemical CO 2 reduction reactions. Recent reports are summarized with an emphasis on the nature and significance of post-mortem and in situ characterization for the proper identification of active sites. We demonstrate that molecular and material structures determine whether electrocatalysts restructure and how they restructure, that understanding of restructuring processes can help us identify active sites for catalysis, and that this knowledge can be leveraged to design precatalysts that generate highly active catalysts under reaction conditions. In addition, we provide recommended practices for studying the integrity of heterogeneous molecular catalysts during and after CO 2 reduction reactions.

CO2 reduction↗
DOE OSTI2025

Advancing Mn-Based Li-Ion Battery Cathodes via a Partially Cation-Disordered Zigzag-Type Li–Nb–Mn–O Framework

Mn-based Li-ion battery cathodes encompass a great variety of materials structures. Decades of research effort have proven that developing a Mn-based structure featuring a high redox activity, stable cycling, and cost-effectiveness is a longstanding challenge. Motivated by such a need and inspired by the structural diversity of Mn-based cathodes, we develop a partially cation-disordered lithium niobium manganese oxide with a zigzag structure, filling the knowledge gap between zigzag-ordered and fully disordered Li-Mn-based oxides. Electrochemically, the partially disordered cathode greatly unlocks the redox activity of the zigzag lattice and maintains the cycling stability. Mechanism-wise, the partial disordering suppresses the disproportionation reaction of Mn(III) and facilitates a disordered λ-MnO 2 -tetragonal cation-disordered rock salt structural transformation. Furthermore, the work suggests the substantial opportunity of using partial disordering as the key strategy to revive locked-up redox activities and realize new energy storage mechanisms, for the pursuit of high-performance cost-effective battery materials.

25 ENERGY STORAGE↗
DOE OSTI2024

Multidimensional perspectives of geo-epidemiology: from interdisciplinary learning and research to cost–benefit oriented decision-making

Research typically promotes two types of outcomes (inventions and discoveries), which induce a virtuous cycle: something suspected or desired (not previously demonstrated) may become known or feasible once a new tool or procedure is invented and, later, the use of this invention may discover new knowledge. Research also promotes the opposite sequence—from new knowledge to new inventions. This bidirectional process is observed in geo-referenced epidemiology—a field that relates to but may also differ from spatial epidemiology. Geo-epidemiology encompasses several theories and technologies that promote inter/transdisciplinary knowledge integration, education, and research in population health. Based on visual examples derived from geo-referenced studies on epidemics and epizootics, this report demonstrates that this field may extract more (geographically related) information than simple spatial analyses, which then supports more effective and/or less costly interventions. Actual (not simulated) bio-geo-temporal interactions (never captured before the emergence of technologies that analyze geo-referenced data, such as geographical information systems) can now address research questions that relate to several fields, such as Network Theory. Thus, a new opportunity arises before us, which exceeds research: it also demands knowledge integration across disciplines as well as novel educational programs which, to be biomedically and socially justified, should demonstrate cost-effectiveness. Grounded on many bio-temporal-georeferenced examples, this report reviews the literature that supports this hypothesis: novel educational programs that focus on geo-referenced epidemic data may help generate cost-effective policies that prevent or control disease dissemination.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Using low-coverage whole genome sequencing (genome skimming) to delineate three introgressed species of buffalofish ( Ictiobus )

Consumption of buffalofish has been sporadically associated with Haff disease-like illnesses involving sudden onset muscle pain and weakness due to skeletal muscle rhabdomyolysis, but determination of precisely which species are associated with these illnesses has been impeded by a lack of species-specific DNA-based markers. Here, three closely related species of buffalofish native to the Mississippi River Basin (Ictiobus bubalus, Ictiobus cyprinellus and Ictiobus niger) that have previously proven genetically indistinguishable using both mitochondrial and nuclear single-locus sequencing were reliably discriminated using low-coverage whole genome sequencing (‘genome skimming’). Using 44 specimens representing the three species collected from the mid/upper (Missouri) and lower (Louisiana) regions of the species’ native ranges, the SISRS (Site Identification from Short Read Sequences) bioinformatics pipeline was adapted to (1) identify over 620Mbp of putatively homologous nuclear sequence data and (2) isolate over 140,000 single-nucleotide polymorphisms (SNPs) that supported accurate species delimitation, all without the use of a reference genome or annotation data. These sites were used to classify Ictiobus spp. samples with genome-skim data, along with a larger set (n = 67) where ultraconserved elements (UCEs) were sequenced. Analyses of whole mitochondrial data revealed more limited signal. Nearly all samples matched their purported species based on morphologic identification, but two Missouri samples morphologically identified as I. niger grouped with samples of I. bubalus, albeit with significant enrichment of I. niger SNPs. To our knowledge this is the first report of a DNA-based tool to reliably discriminate these three morphologically distinct species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Exploration of Domain Aware Machine Learning for Grid Analytics: Transfer-Learnt Energy Models to Assist Buildings Control with Sparse Field Data

Buildings are a primary consumer of energy in the United States and are also increasingly being perceived as providers of grid services such as load shifting, shedding and modulation. High fidelity models of building energy consumption are needed to set appropriate baselines for measurement and verification (M&V) of controllers designed for energy efficient operation of buildings and to enable buildings to provide grid services via. participation in demand response programs. State-of-the-art building energy modeling techniques either rely on Physics based models, or extensive instrumentation of the building envelope to gather “big” data to train machine learning based models such as deep neural networks. While Physics based models are often limited by their accuracy, it is not always feasible to gather a significant amount of field data required to train machine learning based models with sufficient accuracy. In this paper, we explore the use of transfer learning-based strategies to address unsatisfactory accuracy of models for estimating building energy consumption when available field data for training is sparse or of unacceptable quality. In particular, we transfer knowledge in the form of data and parameters, from Physics based simulation frameworks to the field to improve the model accuracy, thus resulting in a Physics-informed Machine Learning framework. We evaluated the efficacy of our approach on field data collected from six commercial buildings and our results indicate that the proposed transfer learning based models provide comparative (and in some cases better) accuracy than state-of-the-art machine learning and deep learning solutions, with just one month of field data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Simulation-based assessment on stochastic load scheduling for building cooling systems

Here, to fill knowledge gaps related to stochastic load scheduling, we performed a comprehensive evaluation of the stochastic load scheduling for building cooling systems. Specifically, we studied the common uncertain variables in the load scheduling process for building cooling systems and categorized those variables based on their dynamic patterns. We then developed a generic stochastic load scheduling framework and applied it to building cooling systems that served a simulated community. This community consists of 100 heterogeneous houses and serves as a virtual testbed for evaluating the performance of stochastic load scheduling. In this evaluation, we considered representatives of uncertain variables with different dynamic patterns and included 100 realizations of the considered uncertainty in the evaluation to better catch the probability distribution of the control performance. The evaluation results suggest that deterministic load scheduling can reduce the operating energy cost by 18% but its performance can be affected by uncertainty. Stochastic load scheduling can further decrease the operating energy cost under uncertainty compared to deterministic load scheduling. We also found that the effectiveness of stochastic load scheduling in handling uncertainty is not directly associated with the number of uncertainty scenarios that are considered in its formulation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗