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NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Deep-learning↗

NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-ofthe-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Hesham ElAbd↗

DOE OSTI2022

Providing Levelized Cost and Waiting Time Inputs for HDV Hydrogen Refueling Station Planning: A Case Study of U.S. I-75 Corridor

Widespread use of diesel fuels in freight transportation leads to greenhouse gas (GHG) emissions that play an important role in air pollution. The air pollution, as well as the energy crisis, drives the transition from diesel fuel to cleaner and more energy-efficient fuels such as hydrogen fuels. With such a transition, the strategic planning and optimization of supporting refueling infrastructure along the national highway for heavy-duty vehicles (HDVs) become a necessity. Cost and service level are two essential factors to be considered in hydrogen refueling station location and capacity optimization. The service level in this study is assessed by the delay (waiting time) at fueling stations as well as the fueling demand fulfilled. To this end, this study presents a methodology to provide waiting time (delay) and levelized hydrogen fuel cost inputs under different station configurations (number of dispensers and fill rates) for hydrogen refueling station planning. Determining the station configurations based on the trade-off between the two inputs is further discussed. Particularly, this study points out that the levelized cost and waiting time are affected by hourly demand patterns and estimates the two inputs under different peak hour fueling demand scenarios. The results suggest that, with the same daily demand, the cost, as well as the waiting time, increases obviously with the peak hour demand. In the case study, the cost grows at least 30% when the peak hour visit frequency of the daily total visits increases from 5% (evenly distributed pattern) to 10% (the most common pattern for existing diesel fueling stations along I-75). Accounting for this peak hour effect in hydrogen refueling station planning is recommended. Overall, the waiting time model and cost analysis provide key inputs for optimizing hydrogen refueling station location and configurations based on anticipated refueling demand patterns. The paper is the second in a series that aims to build a comprehensive modeling plan and optimize hydrogen refueling infrastructure along the Interstate 75 (I-75) corridor for HDVs.

Liu, Yuandong↗

DOE OSTI2026

Optimizing Heat Recovery with Storage: Control Validation and Sensitivity Analysis of the Time-Independent Energy Recovery Plant Using Modelica

Heat recovery in large building central plants saves energy but traditionally requires simultaneous heating and cooling. The Time-Independent Energy Recovery (TIER) plant shifts this paradigm by integrating thermal energy storage (TES) to enable heat recovery regardless of concurrent demand, offering a highly efficient, space-saving solution to achieve California’s energy goals. However, its integration of heat recovery chillers, cooling-only chillers, cooling towers, and trim air-source heat pumps (ASHPs) creates growing control and sizing complexity. To overcome this, this study employs high-fidelity Modelica dynamic simulation to validate TIER control sequences and optimize equipment sizing. We translated the written Sequences of Operation into executable Control Description Language (CDL) to test logic against sub-hourly loads. This verification workflow successfully identified and resolved critical vulnerabilities, such as thermal storage freezing and equipment short-cycling, in a virtual environment prior to physical deployment. Then, the study analyzes TIER plant performance across three simulated building types in three locations, and a real building load profile, ensuring variety in heating and cooling loads, and simultaneity factors and explores sizing rules for the TES and ASHP capacity. The analysis shows that the TIER plant operates equipment efficiently leading to a plant SCOP of around 7.5 across all scenarios, higher than a traditional ASHP plant, and a viable pathway to de-risk complex system design and control through simulation to identify optimal designs that maximize energy efficiency, minimize operational costs, and ensure robust operation in varied environmental conditions, thereby facilitating the broader adoption of such a solution for large buildings.

Zanetti, Ettore↗

DOE OSTI2022

Generating Sequential PV Deployment Scenarios for High Renewable Distribution Grid Planning

This paper introduces a novel approach for generating solar photovoltaic (PV) plant deployment scenarios for grid integration planning. The approach guarantees consistency among scenarios of the same deployment by ensuring that higher penetration scenarios contain PV units deployed in lower penetration scenarios. It also constrains the size and spatial distribution of the PV plants and considers three placement types. A case study on a real-world distribution system proves that the precepts of scenario consistency, deployment diversity, and placement are met. The study further investigates the impact of the resulting scenarios via a stochastic hosting capacity analysis. Results indicate that the ratio between PV and load sizes, referred to as the nodal PV penetration factor (NPPF), is a key driver of the grid integration impact. By reducing the NPPF from 5 to 2, the maximum hosting capacity increased by at least 112%. The study also reveals that scenarios under random placement can lead to higher hosting capacity values.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗

DOE OSTI2022

Generating Sequential PV Deployment Scenarios for High Renewable Distribution Grid Planning: Preprint

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗

DOE OSTI2024

KCl-UCl 3 molten salts investigated by Ab Initio Molecular Dynamics (AIMD) simulations

Ab Initio Molecular Dynamics (AIMD) simulations are performed on molten KCl-UCl3 salt mixtures to determine energies, heat capacities, and densities. The density-dependent energy correction (DFT-dDsC), Grimme et al.'s DFT-D3, and Langreth & Lundqvist (vdW-cx) models are used for dispersion forces and combined with the Perdew-Burke-Ernzerhof (PBE) exchange-correlation potential with a Hubbard U parameter for the 5f electrons of uranium. After validating predictions for the end-member systems to literature data, KCl-UCl 3 mixtures are studied at select temperatures. Densities and energies both deviate from ideal solution behavior, with the maximum deviation occurring around 36% UCl 3 for mixing energies and slightly lower (29% UCl3) for densities. Compared to the NaCl-UCl 3 system, which was previously investigated using the same simulation methodologies, the KCl-UCl 3 density and mixing energy deviations from ideal solution behavior are larger by almost a factor of two. No deviation from ideal solution behavior for heat capacity was observed. The AIMD predictions for mixing energies and densities agree qualitatively with experimental data, though the spread in data obtained from the various dispersion force models utilized, measurements, and empirical estimates makes strong conclusions difficult. The dependence of thermodynamic and thermophysical properties on composition is correlated with the local chemistry of the solution phase, in particular, the tendency of UCl 3 to form network structures.

36 MATERIALS SCIENCE↗

DOE OSTI2024

KCl-UCl 3 molten salts investigated by Ab Initio Molecular Dynamics (AIMD) simulations: A comparative study with three dispersion models

Ab Initio Molecular Dynamics (AIMD) simulations are performed on molten KCl-UCl 3 salt mixtures to determine energies, heat capacities, and densities. The density-dependent energy correction (DFT-dDsC), Grimme et al.’s DFT-D3, and Langreth & Lundqvist (vdW-cx) models are used for dispersion forces and combined with the Perdew-Burke-Ernzerhof (PBE) exchange-correlation potential with a Hubbard U param eter for the 5$f$ electrons of uranium. After validating predictions for the end-member systems to literature data, KCl-UCl 3 mixtures are studied at select temperatures. Densities and energies both deviate from ideal solution behavior, with the maximum deviation occurring around 36% UCl 3 for mixing energies and slightly lower (29% UCl 3 ) for densities. Compared to the NaCl-UCl 3 system, which was previously investigated using the same simulation methodologies, the KCl-UCl 3 density and mixing energy deviations from ideal solution behavior are larger by almost a factor of two. No deviation from ideal solution behavior for heat capacity was observed. The AIMD predictions for mixing energies and densities agree qualitatively with experimental data, though the spread in data obtained from the various dispersion force models utilized, measurements, and empirical estimates makes strong conclusions difficult. The dependence of thermodynamic and thermophysical properties on composition is correlated with the local chemistry of the solution phase, in particular, the tendency of UCl 3 to form network structures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2004

Transforming the NAS: The Next Generation Air Traffic Control System

The next-generation air traffic control system must be designed to safely and efficiently accommodate the large growth of traffic expected in the near future. It should be sufficiently scalable to contend with the factor of 2 or more increase in demand expected by the year 2020. Analysis has shown that the current method of controlling air traffic cannot be scaled up to provide such levels of capacity. Therefore, to achieve a large increase in capacity while also giving pilots increased freedom to optimize their flight trajectories requires a fundamental change in the way air traffic is controlled. The key to achieving a factor of 2 or more increase in airspace capacity is to automate separation monitoring and control and to use an air-ground data link to send trajectories and clearances directly between ground-based and airborne systems. In addition to increasing capacity and offering greater flexibility in the selection of trajectories, this approach also has the potential to increase safety by reducing controller and pilot errors that occur in routine monitoring and voice communication tasks.

Erzberger, Heinz↗

NASA NTRSDate not supplied

Using Systems Engineering to Develop an Integrated Crew Health and Performance System to Mitigate Risk for Human Exploration Missions

New space exploration missions are currently being designed to take humanity beyond low Earth orbit (LEO) to cis-lunar space, the lunar surface, and eventually to Mars. These missions carry increased risks due to a number of factors, including distance from Earth, exposure to deep space hazards, reduced capacity and ability to resupply and evacuate, and increased communication delays. As distance from Earth grows and mission length increases, a growing proportion of mission risk can be attributed to the “human system.” Almost twenty years ago, the Institute of Medicine in the United States recommended the early and complete integration of the human system into the spacecraft and mission design process to mitigate this increased risk inherent in exploration missions. Exploration missions will require increasing levels of crew self-sufficiency that will challenge the current operational paradigms established in LEO. Enabling progressive Earth independence requires management of the increasingly complex interactions among spacecraft systems and integration of all the data and functions that affect human health and performance into one coordinated system –the Crew Health and Performance (CHP) system. This system is a critical spacecraft system that is analogous to other systems such as propulsion, guidance and navigation, or avionics. Design and integration of the CHP system requires the evidence-based merger of typically disparate disciplines such as medicine, human factors, and life support system design, using systems engineering (SE) practices. SE provides traceability of spacecraft requirements and enables reliable and repeatable trade space analysis of the many competing options for hardware and software to be included in the mission architecture. The approach described here enables spacecraft and mission designers to align the scope of a CHP system with mission specific requirements, decrease risk to the crew, and increase the probability of mission success.

Kerry McGuire↗

NASA NTRS2000

Deconditioning and Reconditioning: Humans in Stressful Environments

Deconditioning is an integrated physiological response of the body to a reduction in metabolic rate; that is, to a reduction in energy use or in exercise level. While it may involve assumption of a horizontal body position, it certainly perturbs bodily homeostasis - at least temporarily. The reduction in physical activity that causes deconditioning is often associated with an increase in the time spent, for whatever reason, in a sitting or horizontal position. As a result, orthostatic factors may also contribute to the deconditioning mechanism. The word decondition may be defined as "1: to cause extinction of (a conditioned response) 2: to cause to lose physical fitness". This definition implies that psychological/emotional factors may accompany physical deconditioning, and it is this interpretation of the word that is used throughout this volume. It is apparent that deconditioning plays a major role in the mechanism of the general adaptive (homeostatic) response that is initiated by exposure to prolonged bed rest (BR). And the total homeostatic response to BR involves more than deconditioning per se. For example, it has been shown that the restoration of plasma volume and maximal work capacity after 4 weeks of BR deconditioning left other bodily functions (submaximal exercise oxygen uptake and cardiac output, leg proprioception and posterior leg muscle thickness and volume, head-up tilt tolerance, and sleep quality) functioning at decreased levels. The precise effect of deconditioning on BR homeostasis is difficult to determine, because the fundamental interactive neuro-endocrine-immune control networks that facilitate conditioning and deconditioning also act to maintain basic wholebody homeostasis. For example, is the mechanism of BR-induced deconditioning independent of the mechanism that provokes concomitant orthostatic intolerance; that is, fainting? Assumption of the recumbent body position for prolonged periods of time, results in a new adaptive-homeostatic state. This state occurs in response to the mutually interactive effects of the change in bodily position (hydrostatic pressure), to the virtual elimination of longitudinal pressure on the bones, to the increased confinement with possible reduction in total daily energy (exercise) expenditure, to the reorientation of stimuli within the vestibular organs, and (often) to altered socio-psychological conditions. The exercise-training (reconditioning) syndrome affects total body homeostasis by facilitating increases in work capacity and endurance, whereas deconditioning decreases physical performance. There are many interrelated factors that influence the control parameters that seek to maintain the adaptive conditioning-deconditioning syndrome. These control parameters can be better elucidated by subjecting otherwise healthy ambulatory people to various stresses, such as exercise training and prolonged spaceflight, bed rest, water immersion, hyperbaria, and isolation and confinement. Changes in control parameters will be manifested in muscle function, orthostatic tolerance, cardiorespiratory responses, musculo-skeletal systems, free-radical processes, and body thermoregulation with overarching effects on the subjects' psycho-sociological states. A discussion of these factors and the control parameters constitutes the substance of this volume. Special emphasis is placed on delineating practical applications of the findings that will be of special interest to physicians, nurses, and other health-care workers.

Greenleaf, John E.↗

DOE OSTI2026

The Influence of Mesoscale Particle Structure on the Electrode Degradation and Resultant Electrochemistry of Lithium Ion Cells With Nickel Rich Lithium Nickel Manganese Cobalt Oxide Positive Electrodes

Nickel-rich lithium nickel manganese cobalt oxide, NMC (LiNi x Mn y Co z O 2 ), materials are desirable positive electrodes in lithium ion batteries, providing high capacity and energy density. The mesoscale structure of NMC materials is commonly a polycrystalline aggregate, providing opportunity for short lithium ion transport distance of the small primary particles, yet facile material handling due to the larger secondary particles. On (de)lithiation the NMC unit cell changes volume, where the anisotropic strain can result in secondary particle fracture. This secondary particle fracture process during cycling has been associated with several degradation modes of NMC materials in LIBs. In this work, a milling process was determined whereby the secondary particles could be pre-fractured with retention of the parent primary particle crystallographic structure, crystallite size, and morphology, providing the ability to unambiguously determine the influence of NMC secondary particle size and mesostructure on resultant functional behavior. The bulk and interfacial properties and electrochemistry of as-received commercially obtained polycrystal NMC811 (LiNi 0.8 Mn 0.1 Co 0.1 O 2 , PC NMC) and its milled MPC NMC counterpart were compared. The smaller secondary particle size, higher surface area MPC NMC was found to result in 1) greater cathode tortuosity, 2) reduced surface Ni on the cycled MPC cathodes consistent with surface reconstruction, and 3) increased Ni deposition on the anodes from cathode-anode crosstalk. These factors manifested in unfavorable electrochemical behavior of MPC with lower functional capacity at both 1C and C/10 rates and higher impedance over extended moderate voltage (dis)charge cycling.

36 MATERIALS SCIENCE↗

NASA NTRS1988

Photovoltaics for high capacity space power systems

The anticipated energy requirements of future space missions will grow by factors approaching 100 or more, particularly as a permanent manned presence is established in space. The advances that can be expected in solar array performance and lifetime, when coupled with advanced, high energy density storage batteries and/or fuel cells, will continue to make photovoltaic energy conversion a viable power generating option for the large systems of the future. The specific technologies required to satisfy any particular set of power requirements will vary from mission to mission. Nonetheless, in almost all cases the technology push will be toward lighter weight and higher efficiency, whether of solar arrays or storage devices. This paper will describe the content and direction of the current NASA program in space photovoltaic technology. The paper will also discuss projected system level capabilities of photovoltaic power systems in the context of some of the new mission opportunities under study by NASA, such as a manned lunar base, and a manned visit to Mars.

Flood, Dennis J.↗

NASA NTRS1988

Photovoltaics for high capacity space power systems

The anticipated energy requirements of future space missions will grow by factors approaching 100 or more, particularly as a permanent manned presence is established in space. The advances that can be expected in solar array performance and lifetime, when coupled with advanced, high energy density storage batteries and/or fuel cells, will continue to make photovoltaic energy conversion a viable power generating option for the large systems of the future. The specific technologies required to satisfy any particular set of power requirements will vary from mission to mission. Nonetheless, in almost all cases the technology push will be toward lighter weight and higher efficiency, whether of solar arrays of storage devices. This paper will describe the content and direction of the current NASA program in space photovoltaic technology. The paper will also discuss projected system level capabilities of photovoltaic power systems in the context of some of the new mission opportunities under study by NASA, such as a manned lunar base, and a manned visit to Mars.

Flood, Dennis J.↗

NASA NTRS1983

Expected cycle life vs. depth of discharge relationships of well-behaved single cells and cell strings

The present investigation is concerned with the factors which might influence the cycle life vs. depth of discharge relationship, taking into account the rate of loss of cell capacity, the amount of excess capacity built into the cells, and the penalty in capacity loss resulting from the use of deep depths of discharge. 'First principles' are used to develop a cell life model for somewhat arbitrary conditions. This model is then used to estimate the cycle life vs. depth of discharge relationships for 'well behaved' cells. The stochastic variations associated with groupings of single cells are then introduced to the battery pack cycle life model. The term 'well behaved' cell is used to describe a single cell which does not suffer any abrupt failure mode during the course of its operation. It gradually loses capacity for any number of the usual reasons at a rate which is the product of the fractional depth of discharge and a factor which is characteristic of the cell under consideration.

Thaller, L. H.↗

DOE OSTI2020

Future material demand for automotive lithium-based batteries

The world is shifting to electric vehicles to mitigate climate change. Here, we quantify the future demand for key battery materials, considering potential electric vehicle fleet and battery chemistry developments as well as second-use and recycling of electric vehicle batteries. We find that in a lithium nickel cobalt manganese oxide dominated battery scenario, demand is estimated to increase by factors of 18–20 for lithium, 17–19 for cobalt, 28–31 for nickel, and 15–20 for most other materials from 2020 to 2050, requiring a drastic expansion of lithium, cobalt, and nickel supply chains and likely additional resource discovery. However, uncertainties are large. Key factors are the development of the electric vehicles fleet and battery capacity requirements per vehicle. If other battery chemistries were used at large scale, e.g. lithium iron phosphate or novel lithium-sulphur or lithium-air batteries, the demand for cobalt and nickel would be substantially smaller. Closed-loop recycling plays a minor, but increasingly important role for reducing primary material demand until 2050, however, advances in recycling are necessary to economically recover battery-grade materials from end-of-life batteries. Second-use of electric vehicles batteries further delays recycling potentials.

25 ENERGY STORAGE↗

DOE OSTI2021

SIRIUS: Science-Driven Data Management for Multi-Tiered Storage

The data sets being generated by large applications on very large-scale systems are increasing in both size and complexity. At the same time, there are new ways available to store and access these data sets. The goal in this project is to develop software that applications can use to make use of new and existing storage technologies in more sophisticated ways. One challenge in scientific data management is handling ‘hot’ vs ‘cold’ data. Data that is hot is data that is needed (or will be needed soon) in order for the program to continue progressing, while cold data is either output (and so will not be need further during the life of the program) or will not be needed until significantly later in the program’s run. Hot data should be stored in a way that allows fast access. On most systems, economic factors lead to an inverse relationship between storage performance and storage capacity and so fast access storage is limited. This makes it important to correctly place hot and cold data and avoid cold data unnecessarily consuming precious resources. In this reporting period, we addressed this challenge in various ways and at various levels. Data management frameworks offer only limited control to applications in how data is stored. We have added software capabilities for seamlessly moving data between layers of the storage technology using promote and demote functions to existing software frameworks. This gives direct control to applications in deciding what priority data receives. Additionally, we integrated different storage layer management frameworks in order to allow data to be exchanged and moved between storage layers in a consistent way across the application. Further, applications are not always able to directly decide what storage level makes sense for a given piece of data without an understanding of the underlying storage technologies. Data storage frameworks are often positioned to make these sorts of decisions in service of the application. We have added machine-learning based capabilities to data staging frameworks in order to make intelligent decisions about where data should be stored given learning about patterns in previous usage of similar data.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1981

A simulation study of the recession coefficient for antecedent precipitation index

The antecedent precipitation index (API) is a useful indicator of soil moisture conditions for watershed runoff calculations and recent attempts to correlate this index with spaceborne microwave observations have been fairly successful. It is shown that the prognostic equation for soil moisture used in some of the atmospheric general circulation models together with Thornthwaite-Mather parameterization of actual evapotranspiration leads to API equations. The recession coefficient for API is found to depend on climatic factors through potential evapotranspiration and on soil texture through the field capacity and the permanent wilting point. Climatologial data for Wisconsin together with a recently developed model for global isolation are used to simulate the annual trend of the recession coefficient. Good quantitative agreement is shown with the observed trend at Fennimore and Colby watersheds in Wisconsin. It is suggested that API could be a unifying vocabulary for watershed and atmospheric general circulation modelars.

Choudhury, B. J.↗