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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
NASA NTRS1980

Low-cost conversion of polycrystalline silicon into sheet by HEM and FAST

The conversion of polycrystalline silicon to sheet form (the wafers produced are 10 cm x 10 cm cross section with minimum surface damage) by the Heat Exchanger Method (HEM) and multi-wire Fixed Abrasive Slicing Technique (FAST), as a means of reducing the cost of solar arrays for adaptation of photovoltaic technology for terrestrial applications, is given. A schematic of a HEM furnace, which includes a silica crucible, and developments in the HEM process are presented. A new machine for slicing with wire was designed and fabricated. The high-speed slicer has been used to slice 19 wafers per cm from 10 cm diameter crystals. Both HEM and FAST are low-cost processes and they have the potential of giving one of the lowest add-on costs ($6.24 and $6.48 per square meter of sheet respectively, with the combination add-on cost of $14.87 per square meter) of this conversion.

Khattak, C. P.↗
NASA NTRS1981

Silicon Ingot Casting - Heat Exchanger Method (HEM). Multi-Wire Slicing - Fixed Abrasive Slicing Technique (Fast). Phase 4 Silicon Sheet Growth Development for the Large Area Sheet Task of the Low-Cost Solar Array Project

The crystallinity of large HEM silicon ingots as a function of heat flow conditions is investigated. A balanced heat flow at the bottom of the ingot restricts spurious nucleation to the edge of the melted-back seed in contact with the crucible. Homogeneous resistivity distribution over all the ingot has been achieved. The positioning of diamonds electroplated on wirepacks used to slice silicon crystals is considered. The electroplating of diamonds on only the cutting edge is described and the improved slicing performance of these wires evaluated. An economic analysis of value added costs of HEM ingot casting and band saw sectioning indicates the projected add on cost of HEM is well below the 1986 allocation.

Schmid, F.↗
NASA NTRS1982

An EBIC study of HEM polycrystalline silicon

Low-cost silicon for solar cells grown by the heat exchanger method (HEM) was studied in the electron beam induced current (EBIC) mode of a scanning electron microscope (SEM). Comparisons were made between the defects observed optically and the recombination centers visible in EBIC. Much of the HEM material was single crystalline, but structural defects were found from areas near the corners of the grown material. Most of these defects consisted of linear twin boundaries and grain boundaries. The electrical activity of these boundaries was dependent on symmetry of the boundaries. Symmetric twin boundaries did not exhibit recombination activity while unsymmetric twin boundaries were electrically active.

Koch, T.↗
NASA NTRS1981

Characterization of HEM silicon for solar cells

The Heat Exchanger Method (HEM) is a promising low-cost ingot casting process for material used for solar cells. This is the only method that is capable of casting single crystal ingots with a square cross section using a directional solidification technique. This paper describes the chemical, mechanical and electrical properties of the HEM silicon material as a function of position within the ingot.

Dumas, K. A.↗
NASA NTRS1982

Part 1: Silicon ingot casting. Heat Exchanger Method (HEM)

Directional solidification by the Heat Exchanger Method (HEM) is a viable approach for directional solidification of silicon ingots used for terrestrial solar cell applications. Some of the significant advances made during this phase of the program were: ingot size, 34 cm x 34 cm x 17 cm - 45 kg, usable material yield - 90%, solar cell efficiency averaged over the whole ingot - 85% of control CZ (35 kg ingot), very uniform resistivity over the boule, and cycle time for 36 kg ingots - 56 hours. Some of the problems encountered were growth rate decrease with increased ingot height, silicon carbide precipitates, and high dislocation density in HEM material. The silicon carbide is attributed to backstreaming of oil vapors from the vacuum pump; the high dislocation density is associated with the thermal history of the boule.

Source record↗
NASA NTRS1982

Single crystal growth of upgraded metallurgical silicon by HEM for photovoltaic applications

Commercially available metallurgical grade (MG) silicon has high B and P content which is not reduced significantly by directional solidification. By choosing high purity raw materials for an experimental Submerged Electrode Arc Furnace, most of the impurities are reduced to 10 ppmw. Directional solidification of upgraded metallurgical grade (UMG) silicon by the Heat Exchanger Method (HEM) has produced 16 cm x 16 cm cross section ingots with nearly single crystal structure. The main problem encountered during directional solidification was SiC impurities dispersed through the structure. Solar cells fabricated from UMG silicon that was directionally solidified twice by HEM have shown up to 12.33% (am1) conversion efficiency.

Khattak, C. P.↗
DOE OSTI2024

HEM (HolisticElectricityModel.jl) [SWR-21-62]

The Holistic Electricity Model (HEM) is a computational framework for analyzing electricity systems in their entirety, from the points of view of all key stakeholders. Related publication: https://ieeexplore.ieee.org/document/10128887

Hale, Elaine↗
DOE OSTI2020

RL-HEMS: Reinforcement Learning Based Home Energy Management System for HVAC Energy Optimization

Heating, ventilation, and air conditioning (HVAC) is one of the major energy consumers in the residential sector. It is important to be able to monitor and control the energy consumed to provide utility services such as load shaping while satisfying the comfort and economic constraints of the homeowner. The objective of this work is to create the optimal schedule for HVAC operation to reduce the cost while satisfying the home-owner and equipment’s constraints using a model-free Reinforcement Learning (RL)-based optimization. The specific goal is to find the right balance between reducing energy cost, consumption, and customer comfort level. Our research effort addresses this optimization problem using multiple components: the development of initial learning test-bed and implementation of RL techniques on a real home. This will enable the rapid evaluation of the RL techniques and provide an early baseline to train before implementation on site. The RL algorithm is designed to learn the energy use patterns and generate the optimized schedule for HVAC within an acceptable time-interval to satisfy the homeowner’s comfort and minimize the energy usage. Our preliminary results are promising and we have observed a 17% reduction in the total cost and a 15% reduction in the power utilization using our RL-based HVAC model—RL-HEMS.

Kotevska, Olivera↗
DOE OSTI2022

I ntera C hem : Exploring Excited States in Virtual Reality with Ab Initio Interactive Molecular Dynamics

InteraChem is an ab initio interactive molecular dynamics (AI-IMD) visualizer that leverages recent advances in virtual reality hardware and software, as well as the graphical processing unit (GPU)-accelerated TeraChem electronic structure package, in order to render quantum chemistry in real time. We introduce the exploration of electronically excited states via AI-IMD using the floating occupation molecular orbital-complete active space configuration interaction method. The optimization tools in InteraChem enable identification of excited state minima as well as minimum energy conical intersections for further characterization of excited state chemistry in small- to medium-sized systems. We demonstrate that finite-temperature Hartree–Fock theory is an efficient method to perform ground state AI-IMD. InteraChem allows users to track electronic properties such as molecular orbitals and bond order in real time, resulting in an interactive visualization tool that aids in the interpretation of excited state chemistry data and makes quantum chemistry more accessible for both research and educational purposes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

?hem-GPT series of AI models

A suite of chemistry GPT models built using Transform Your World are available at DOI, from which one example is mirrored on Hugging Face. https://huggingface.co/asalij/Xhem-GPT-2

Salij, Andrew↗
DOE OSTI2020

Corrosion Control in Carbon Fiber Reinforced Plastic (CFRP) Composite-Aluminum Closure Panel Hem Joints

This project targeted the implementation of CFRP/Al closure panels as a drop-in replacement for all-aluminum closures used in automobile production today. The project focused on the development of corrosion fundamentals involving CFRP materials and the use of those materials in mixed-material joints. Numerous studies were completed to benchmark current materials and test mixed metal joints. Good correlation was found between outdoor exposures and laboratory cyclic corrosion testing. The insights from the corrosion testing fundamentals and benchmarking phases informed the development of low-cure adhesive and electrocoats and conductive primers for the CFRP. Adhesive and electrocoat materials were successfully developed meeting low-cure targets for bake (150C/10min). These materials were scaled-up and used to produce 5 prototype liftgates to further evaluate CFRP/Al joints in a full-scale part. The lower cure materials enabled mitigation of CTE mismatch and reduced the required bake temperature, resulting in reduced part distortion. Corrosion testing of the liftgates revealed unexpected increases in corrosion relative to the standard bake materials. While rigorous evaluation of the parts was not possible due to numerous complexities associated with prototype testing, the results provided significant insights into the material behavior of CFRP/Al joints. Key learnings from the project included a better understanding of the system complexity (interplay of substrate/primer conductivity, production of full-scale prototypes), advances in coating/adhesive design for low-cure applications involving CFRP, and advancement of corrosion test protocols for CFRP.

36 MATERIALS SCIENCE↗
NASA NTRS2008

Scientific Goals and Objectives for the Human Exploration of Mars: 1. Biology and Atmosphere/Climate

To prepare for the exploration of Mars by humans, as outlined in the new national vision for Space Exploration (VSE), the Mars Exploration Program Analysis Group (MEPAG), chartered by NASA's Mars Exploration Program (MEP), formed a Human Exploration of Mars Science Analysis Group (HEM-SAG), in March 2007. HEM-SAG was chartered to develop the scientific goals and objectives for the human exploration of Mars based on the Mars Scientific Goals, Objectives, Investigations, and Priorities.1 The HEM-SAG is one of several humans to Mars scientific, engineering and mission architecture studies chartered in 2007 to support NASA s plans for the human exploration of Mars. The HEM-SAG is composed of about 30 Mars scientists representing the disciplines of Mars biology, climate/atmosphere, geology and geophysics from the U.S., Canada, England, France, Italy and Spain. MEPAG selected Drs. James B. Garvin (NASA Goddard Space Flight Center) and Joel S. Levine (NASA Langley Research Center) to serve as HEMSAG co-chairs. The HEM-SAG team conducted 20 telecons and convened three face-to-face meetings from March through October 2007. The management of MEP and MEPAG were briefed on the HEM-SAG interim findings in May. The HEM-SAG final report was presented on-line to the full MEPAG membership and was presented at the MEPAG meeting on February 20-21, 2008. This presentation will outline the HEM-SAG biology and climate/atmosphere goals and objectives. A companion paper will outline the HEM-SAG geology and geophysics goals and objectives.

Levine, Joel S.↗
DOE OSTI2021

Sensitivity Analysis of Occupant Preferences on Energy Usage in Residential Buildings: Preprint

Residential buildings, accounting for 37% of the total electricity consumption in the United States, are suitable for demand-side management (DSM) programs to support effective and economical operation of the power system. A home energy management system (HEMS) enables residential buildings to participate in such programs. It is important to account for occupant preferences in HEMS to ensure occupant satisfaction while participating in DSM programs. For example, people who prefer a higher thermal comfort level are likely to consume more energy. In this study, we used foresee™, a HEMS developed by the National Renewable Energy Lab (NREL), to perform a sensitivity analysis of occupant preferences with the following objectives: minimize utility cost, minimize carbon footprint, and maximize thermal comfort. To incorporate the preferences into the HEMS, the SMARTER method was used to derive a set of weighting factors for each objective. We performed week-long building energy simulations using a model of a home in Fort Collins, Colorado, where there is mandatory time-of-use electricity rate structure. The foreseeTM HEMS was used to control the home with six different sets of occupant preferences. The study shows that occupant preferences can have a significant impact and is important to consider when modeling residential buildings. Results show that the HEMS could achieve energy reduction ranging from 3% to 21%, cost savings ranging from 5% to 24%, and carbon emission reduction ranging from 3% to 21%, while maintaining a low thermal discomfort level ranging from 0.78 K-hour to 6.47 K-hour in a one-week period during winter. These outcomes quantify the impact of varying occupant preferences and will be useful for controlling the electrical grid and developing HEMS solutions.

carbon footprint↗
DOE OSTI2024

Mixed Enthalpy–Entropy Descriptor for the Rational Design of Synthesizable High-Entropy Materials Over Vast Chemical Spaces

The practically unlimited high-dimensional composition space of high-entropy materials (HEMs) has emerged as an exciting platform for functional material design and discovery. However, the identification of stable and synthesizable HEMs and robust design rules remains a daunting challenge. Here, we propose a mixed enthalpy–entropy descriptor (MEED) that enables highly efficient, robust, high-throughput prediction of synthesizable HEMs across vast chemical spaces from first-principles. The MEED is based on two parameters: the relative formation enthalpy with respect to the most stable competing compound and the spread of the point-defect formation energy spectrum. The former measures the relative synthesizability of an HEM to its most stable competing phase, going beyond the conventional thermodynamic understanding. Further, the latter gauges the relative entropy forming ability of an HEM, entailing no sampling over numerous alloy configurations. By applying the MEED to two structurally distinct representative material systems (i.e., 3D rocksalt carbides and 2D layered sulfides), we not only successfully identify all experimentally reported HEMs within these systems but also reveal a cutoff criterion for assessing their relative synthesizability within each system. By the MEED, tens of new high-entropy carbides and 2D high-entropy sulfides are also predicted, which have the potential for a wide variety of applications such as coating in aerospace devices, energy conversion and storage, and flexible electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High entropy powering green energy: hydrogen, batteries, electronics, and catalysis

A reformation in energy is underway to replace fossil fuels with renewable sources, driven by the development of new, robust, and multi-functional materials. High-entropy materials (HEMs) have emerged as promising candidates for various green energy applications, having unusual chemistries that give rise to remarkable functionalities. This review examines recent innovations in HEMs, focusing on hydrogen generation/storage, fuel cells, batteries, semiconductors/electronics, and catalysis—where HEMs have demonstrated the ability to outperform state-of-the-art materials. We present new master plots that illustrate the superior performance of HEMs compared to conventional systems for hydrogen generation/storage and heat-to-electricity conversion. We highlight the role of computational methods, such as density functional theory and machine learning, in accelerating the discovery and optimization of HEMs. The review also presents current challenges and proposes future directions for the field. We emphasize the need for continued integration of modeling, data, and experiments to investigate and leverage the underlying mechanisms of the HEMs that are powering progress in sustainable energy.

batteries↗