Search NASA⌕ Search

SEARCH · Search NASA

Results for “cheaper”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records

Faster, Cheaper, Better Way to Make DNA

Two graduate students at the Department of Energy’s Joint BioEnergy Institute (JBEI) based at Berkeley Lab have pioneered a new way to synthesize DNA sequences through a creative use of enzymes that promises to be faster, cheaper, and more accurate. DNA synthesis is a fundamental tool in the rapidly growing field of synthetic biology, in which organisms can be engineered to do things like decompose plastic and manufacture biofuels and medicines. This discovery could dramatically accelerate the pace of scientific discovery.

LBNL, Berkeley Lab↗

Faster, Cheaper, Better Way to Make DNA

Two graduate students at the Department of Energy’s Joint BioEnergy Institute (JBEI) based at Berkeley Lab have pioneered a new way to synthesize DNA sequences through a creative use of enzymes that promises to be faster, cheaper, and more accurate. DNA synthesis is a fundamental tool in the rapidly growing field of synthetic biology, in which organisms can be engineered to do things like decompose plastic and manufacture biofuels and medicines. This discovery could dramatically accelerate the pace of scientific discovery. Joint BioEnergy Institute -- https://www.jbei.org/

59 BASIC BIOLOGICAL SCIENCES↗

Cheaper and more noise-resilient quantum state preparation using eigenvector continuation

Subspace methods are powerful, noise-resilient methods that can effectively prepare ground states on quantum computers. The challenge is to get a subspace with a small condition number that spans the states of interest using minimal quantum resources. In this work, we will use eigenvector continuation to build a subspace from the low-lying states of a set of Hamiltonians. The basis vectors are prepared using truncated versions of standard state preparation methods such as imaginary-time evolution (ITE), adiabatic state preparation (ASP), and variational quantum eigensolver. By using these truncated methods combined with eigenvector continuation, we can directly improve upon them, obtaining more accurate ground-state energies at a reduced cost. We use several spin systems to demonstrate convergence even when methods like ITE and ASP fail, such as ASP in the presence of level crossings and ITE with vanishing energy gaps. We also showcase the noise resilience of this approach beyond the gains already made by having shallower quantum circuits. Furthermore, our findings suggest that eigenvector continuation can be used to improve existing state preparation methods in the near term.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Battery Electric Vehicles can reduce greenhouse has emissions and make renewable energy cheaper in India

India's National Mission on Electric Mobility (NMEM) sets a countrywide goal of deploying 6 to 7 million hybrid and electric vehicles (EVs) by 2020. There are widespread concerns, both within and outside the government, that the Indian grid is not equipped to accommodate additional power demand from battery electric vehicles (BEVs). Such concerns are justified on the grounds of India's notorious power sector problems pertaining to grid instability and chronic blackouts. Studies have claimed that deploying BEVs in India will only

33 ADVANCED PROPULSION SYSTEMS↗

Organic acid production from food wastes using Gluconobacter oxydans: A possible source of cheaper lixiviants for leaching REE from end-of-life products

Gluconobacter oxydans is an acidophilic bacterium that is used in industry for producing organic acids, such as gluconic acid, from sugars such as glucose. Recently it has been discovered that the organic acids produced by G. oxydans has potential to be used in the recovery of rare earth elements (REE), especially gluconic acid. Producing gluconic acid from pure glucose has been deemed costly and so research has been conducted to find more economically friendly feedstock. In this work corn stover, potato peel waste, market rejected fruits, and apple processing and holding pond waters were investigated for their ability to provide G. oxydans sufficient sugar to grow and produce organic acid for bioleaching. Substrates such as the corn stover and fruits provided more than 20 g/L of glucose, and the bacterium grew well on all but the apple processing and holding pond water.

59 BASIC BIOLOGICAL SCIENCES↗

ISO 50001 and SEP Faster and Cheaper - Exploring the Enterprise-Wide Approach

ISO 50001 and other management systems (e.g., ISO 9001 and ISO 14001) allow for implementation and certification at the enterprise level. The "Central Office" concept, which allows a small group of employees to manage and facilitate the organization’s energy management system (EnMS) at the enterprise level, was introduced within the ISO 50003 standard to provide guidance to ISO 50001 certification bodies. Four industrial companies have partnered with the United States Department of Energy to pilot the enterprise-wide ISO 50001/SEP concept under the Better Buildings Superior Energy Performance (SEP) Enterprise-wide Accelerator. Each organization developed a Central Office to host their EnMS while implementing ISO 50001/SEP at multiple physically separated sites. The four corporate partners tailored their Central Office implementation model to meet their own specific circumstances and needs. This paper reviews the commonalities, differences, and benefits of each of these enterprise-wide implementation models, including organizational structures, Central Office staff responsibilities, and key strategies. The cost savings and benefits of using the enterprise-wide approach were assessed, including the cost per site compared with that of a conventional, single-site ISO 50001/SEP implementation approach. This paper also discusses the drivers for the cost reductions realized through these enterprise-wide approaches. The four partner companies worked with 30 total sites. On average, these 30 sites improved energy performance by 5% annually over their SEP achievement periods, saved more than $600,000 annually in energy costs and reduced implementation cost for ISO 50001 and SEP by $19,000 and 0.8 Full Time Equivalent × years (FTE-yr) of staff time per site. The results can inform other organizations seeking to implement enterprise-wide ISO 50001/SEP, as well as energy efficiency organizations seeking to promote wider adoption of ISO 50001 implementation.

97 MATHEMATICS AND COMPUTING↗

Data Movement Dominates: Final Report

Over the past three years in this project, what we have observed is that the primary reason for data movement in large-scale systems is that the per-node capacity is not large enough—i.e., one of the solutions to the data-movement problem (certainly not the only solution that is required, but a significant one nonetheless) is to increase per-node capacity so that inter-node traffic is reduced. This unfortunately is not as simple as it sounds. Today’s main memory systems for datacenters, enterprise computing systems, and supercomputers, fail to provide high per-socket capacity [Dirik & Jacob 2009; Cooper-Balis et al. 2012], except at extremely high price points (factors of 10–100x the cost/bit of consumer main-memory systems) [Stokes 2008]. The reason is that our choice of technology for today’s main memory systems—i.e., DRAM, which we have used as a main-memory technology since the 1970s [Jacob et al. 2007]—can no longer keep up with our needs for density and price per bit. Main memory systems have always been built from the cheapest, densest, lowest-power memory technology available, and DRAM is no longer the cheapest, the densest, nor the lowest-power storage technology out there. It is now time for DRAM to go the way that SRAM went: move out of the way for a cheaper, slower, denser storage technology, and become a cache instead. This inflection point has happened before, in the context of SRAM yielding to DRAM. There was once a time that SRAM was the storage technology of choice for all main memories [Tomasulo 1967; Thornton 1970; Kidder 1981]. However, once DRAM hit volume production in the 1970s and 80s, it supplanted SRAM as a main memory technology because it was cheaper, and it was denser. It also happened to be lower power, but that was not the primary consideration of the day. At the time, it was recognized that DRAM was much slower than SRAM, but it was only at the supercomputer level (For instance the Cray X-MP in the 1980s and its follow-on, the Cray Y-MP, in the 1990s) that could one afford to build ever- larger main memories out of SRAM—the reasoning for moving to DRAM was that an appropriately designed memory hierarchy, built of DRAM as main memory and SRAM as a cache, would approach the performance of SRAM, at the price-per-bit of DRAM [Mashey 1999]. Today it is quite clear that, were one to build an entire multi-gigabyte main memory out of SRAM instead of DRAM, one could improve the performance of almost any computer system by up to an order of magnitude—but this option is not even considered, because to build that system would be prohibitively expensive. It is now time to revisit the same design choice in the context of modern technologies and modern systems. For reasons both technical and economic, we can no longer afford to build ever-larger main memory systems out of DRAM. Flash memory, on the other hand, is significantly cheaper and denser than DRAM and therefore should take its place. While it is true that flash is significantly slower than DRAM, one can afford to build much larger main memories out of flash than out of DRAM, and we show that an appropriately designed memory hierarchy, built of flash as main memory and DRAM as a cache, will approach the performance of DRAM, at the price-per-bit of flash. In our studies as part of this project, we have investigated Non-Volatile Main Memory (NVMM), a new main-memory architecture for large-scale computing systems, one that is specifically designed to address the weaknesses described previously. In particular, it provides the following features: non-volatility: The bulk of the storage is comprised of NAND flash, and in this organization DRAM is used only as a cache, not as main memory. Furthermore, the flash is journaled, which means that operations such as checkpoint/restore are already built into the system. 1+ terabytes of storage per socket: SSDs and DRAM DIMMs have roughly the same form factor (several square inches of PCB surface area), and terabyte SSDs are now commonplace. performance approaching that of DRAM: DRAM is used as a cache to the flash system. price-per-bit approaching that of NAND: Flash is currently well under $0.50 per gigabyte; DDR3 SDRAM is currently just over $10 per gigabyte [Newegg 2014]. Even today, one can build an easily affordable main memory system with a terabyte or more of NAND storage per CPU socket (which would be extremely expensive were one to use DRAM), and our cycle- accurate, full-system experiments show that this can be done at a performance point that lies within a factor of two of DRAM.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Data-driven wind turbine wake modeling via probabilistic machine learning

Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions and the interaction between wakes. Physics-based models that capture the wake flow field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced-order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional latent space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.

Deep neural networks↗

Sustainable hydrogen manufacturing via renewable-integrated intensified process for refueling stations

The widescale consumer adoption of hydrogen fuel cell electric vehicles (HFCEVs) is currently hindered by the high cost of small-scale hydrogen generation and the lack of extensive hydrogen refueling infrastructure. Natural gas-based hydrogen is cheaper when produced in large volumes but is also associated with high CO 2 emissions. To counter these challenges, we propose a hybrid approach where both natural gas and renewables are integrated in a synergistic manner using a dynamic process intensification technology that can be deployed on-site for meeting local demands of refueling stations. The technology is based on sorption enhanced steam methane reforming (SE-SMR) that utilizes a combination of reaction with in-situ CO 2 adsorption for enhancing process modularity, productivity and efficiency thereby outperforming conventional SMR at small scale. We develop a mixed integer linear programming (MILP)-based optimization framework for simultaneous design and scheduling of the SE-SMR process. The simultaneous optimization provides a synergistic combination whereby the renewables allow sustainable hydrogen manufacturing and the dynamic SE-SMR allows optimal use of the intermittency of the renewables. The U.S. nationwide analysis indicates that for futuristic renewable prices and a hydrogen production capacity of 2 ton/day, hydrogen can be produced at 50% less cost compared to the current cost of small-scale hydrogen generation. Finally, the city-wise analysis with varying hydrogen demand shows that even with just 5% HFCEV market penetration level, hydrogen production cost less than $3/kg can be obtained at small scales across the United States with even cheaper hydrogen for large cities.

08 HYDROGEN↗

Surface enrichment of Pt in stable Pt-Ir nano-alloy particles on MgAl 2 O 4 spinel in oxidizing atmosphere

With the capability of MgAl2O4 spinel {111} nano-facets in stabilizing small Rh, Ir and Pt particles, bimetallic Ir-Pt catalysts on the same support were investigated, aiming at further lowering the catalyst cost by substituting expensive Pt with cheaper Ir in the bulk. Small Pt-Ir nano-alloy particles (< 2nm) were successfully stabilized on the spinel {111} nano-facets as expected. Interestingly, methanol oxidative dehydrogenation (ODH) rate on the surface Pt atoms increases with oxidizing aging but decreases upon reducing treatment, where Ir is almost inactive under the same reaction conditions. Up to three times enhancement in Pt exposure was achieved when the sample was oxidized at 800 °C in air for 1 week and subsequently reduced by H2 for 2 h, demonstrating successful surface enrichment of Pt on Pt-Ir nano-alloy particles. A dynamic stabilization mechanism involving wetting\nucleation seems to be responsible for the evolution of surface compositions upon cyclic oxidizing and reducing ther-mal treatments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A technoeconomic analysis of poly- and single-crystalline NMCxyz from material synthesis to battery pack design

A process model was developed for estimating the cost of manufacturing lithium nickel manganese cobalt oxide (LiNi x Mn y Co z O 2 , NMCxyz), the main cathode active material in lithium-ion batteries used for electric vehicles in the United States. The model was used to estimate the prices of NMC622, NMC811, and NMC955 with poly- and single-crystalline morphologies. The Battery Performance and Cost Model (BatPaC) was used to translate the NMC prices into battery pack prices. A decrease in cobalt content from NMC622 (20% Co) to NMC955 (5% Ni) decreases the material price by $\$$0.85/kg (−3%) due to a decrease in the cost of battery materials, which account for >60% of the total price. NMC622 only produce cheaper packs if the nickel sulfate cost is > 4 × its baseline, indicating higher nickel materials will lower pack cost under normal market conditions. Single-crystalline materials are $\$$2/kg (+8%) more expensive than their polycrystalline counterparts due to higher manufacturing costs from longer, hotter calcinations in less densely packed saggars. This increases the pack price by ∼$\$$3/kWh, assuming identical electrochemical properties. In conclusion, the single-crystalline materials could yield cheaper packs if cycled to higher upper cutoff voltages (i.e., 4.35 to 4.43 V for the single-crystalline material vs. 4.25 V for their polycrystalline counterparts).

Cost modeling↗