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NASA NTRS1998

The Consortium for Advancing Renewable Energy Technology (CARET)

The Consortium for Advancing Renewable Energy (CARET) is a research and education program which uses the theme of renewable energy to build a minority scientist pipeline. CARET is also a consortium of four universities and NASA Lewis Research Center working together to promote science education and research to minority students using the theme of renewable energy. The consortium membership includes the HBCUs (Historically Black Colleges and Universities), Fisk, Wilberforce and Central State Universities as well as Kent State University and NASA Lewis Research Center. The various stages of this pipeline provide participating students experiences with a different emphasis. Some emphasize building enthusiasm for the classroom study of science and technology while others emphasize the nature of research in these disciplines. Still others focus on relating a practical application to science and technology. And, of great importance to the success of the program are the interfaces between the various stages. Successfully managing these transitions is a requirement for producing trained scientists, engineers and technologists. Presentations describing the CARET program have been given at this year's HBCU Research Conference at the Ohio Aerospace Institute and as a seminar in the Solar Circle Seminar series of the Photovoltaic and Space Environments Branch at NASA Lewis Research Center. In this report, we will describe the many positive achievements toward the fulfillment of the goals and outcomes of our program. We will begin with a description of the interactions among the consortium members and end with a description of the activities of each of the member institutions .

Gordon, E. M.↗

NASA NTRS2014

High Energy Replicated Optics to Explore the Sun Balloon-Borne Telescope: Astrophysical Pointing

On September 21, 2013, the High Energy Replicated Optics to Explore the Sun, or HEROES, balloon-borne x-ray telescope launched from the Columbia Scientific Balloon Facility's site in Ft. Summer, NM. The flight lasted for approximately 27 hours and the observational targets included the Sun and astrophysical sources GRS 1915+105 and the Crab Nebula. Over the past year, the HEROES team upgraded the existing High Energy Replicated Optics (HERO) balloon-borne telescope to make unique scientific measurements of the Sun and astrophysical targets during the same flight. The HEROES Project is a multi-NASA Center effort with team members at both Marshall Space Flight Center (MSFC) and Goddard Space Flight Center (GSFC), and is led by Co-PIs (one at each Center). The HEROES payload consists of the hard X-ray telescope HERO, developed at MSFC, combined with several new systems. To allow the HEROES telescope to make observations of the Sun, a new solar aspect system was added to supplement the existing star camera for fine pointing during both the day and night. A mechanical shutter was added to the star camera to protect it during solar observations and two alignment monitoring systems were added for improved pointing and post-flight data reconstruction. This mission was funded by the NASA HOPE (Hands-On Project Experience) Training Opportunity awarded by the NASA Academy of Program/Project and Engineering Leadership, in partnership with NASA's Science Mission Directorate, Office of the Chief Engineer and Office of the Chief Technologist.

Gaskin, Jessica↗

NASA NTRS2003

Multiple-etalon systems for the Advanced Technology Solar Telescope

Multiple etalon systems are discussed that meet the science requirements for a narrow-passband imaging system for the 4-meter National Solar Observatory (NSO)/Advance Technology Solar Telescope (ATST). A multiple etalon system can provide an imaging interferometer that works in four distinct modes: as a spectro-polarimeter, a filter-vector magnetograph, an intermediate-band imager, and broadband high-resolution imager. Specific dual and triple etalon configurations are described that provide a spectrographic passband of 2.0-3.5 micron and reduce parasitic light levels to 10(exp -4) as required for precise polarization measurement, e.g., Zeeman measurements of magnetic sensitive lines. A TESOS-like (Telecentric Etalon SOlar Spectrometer) triple etalon system provides a spectral purity of 10(exp -5). The triple designs have the advantage of reducing the finesse requirement on each etalon; allow the use of more stable blocking filters, and have very high spectral purity. A dual-etalon double-pass (Cavallini-like) system can provide a competing configuration. Such a dual-etalon design can provide high contrast. The selection of the final focal plane instrument will depend on a trade-off between an ideal instrument and practical reality. The trade study will include the number of etalons, their aperture sizes, complexities of the optical train, number of blocking filters, configuration of the electronic control system, computer interfaces, temperature controllers, etalon controllers, and their associated feedback electronics. The heritage of single and multiple etalon systems comes from their use in several observatories, including the Marshall Space Flight Center (MSFC) Solar Observatory, Sacramento Peak Observatory (NSO), and Kiepenheuer-Institut fur Sonnenphysik (KIS, Germany), Mees Solar Observatory (University of Hawaii), and Arcetri Astrophysical Observatory (Italy). The design of the ATST multiple etalon system will benefit from the experience gained at these observatories.

Gary, G. Allen↗

DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as time series data shifts. In this research, a changepoint detection (CPD) algorithm that automatically detects data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window across the labeled data sets. Pending approval, we plan to release the labeled data sets for this research on NREL's DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package. By supplying the training sets and algorithm, we hope to encourage further development in this research space.

data shift↗

DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗

DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

14 SOLAR ENERGY↗

DOE OSTI2023

Cyberguardians and STEM Warriors (Final Technical Report (FTR))

In the past decade, solar power, with and without energy storage has become the fastest growing source of energy generation in the world. In the U.S., solar employment more than doubled from 105,145 jobs in 2011 to 255,037 jobs in 2021, four times faster than the U.S. job growth rate overall. These factors, combined with technology advancements, creates a skills gap that puts tremendous stress on society to deliver the workers to fill the open job requisitions. The Cyberguardians and STEM Warriors project (Cyberguardians) was designed to address the trained-worker shortage in the energy industry in three ways: 1) by developing educational curriculum that addresses DER technology changes; 2) by delivering curriculum to prospective workers, including military veterans and their families, via universities, community colleges, and vocational training outlets; and 3) introducing individuals who have completed training to employers that can hire them. Cyberguardians exceeded its curriculum goals by producing 27 academic units of university-accredited material (12 total courses) covering energy fundamentals, smart inverters, Distributed Energy Resource (DER) data communication, cybersecurity, standardization, certification, data analytics, and IEEE 1547 standard topics. The North American Board of Certified Energy Practitioners (NABCEP) also accredited the material for use in their credential program. Seven instructors were recruited and trained, and six academic institutions (University of California San Diego, State University of New York, North Carolina State University, Harper Community College, Green Village Academy, and the SunSpec Alliance) were enlisted, meeting program goals. All course material was published under the Creative Commons license and made available royalty free, thus providing a long-lasting public benefit. The program’s outreach program vastly exceeded program goals and incorporated the efforts of 13 outreach partners (11 of which are veteran focused), an advisory board representing 15 companies, webinars and 10’s of thousands of email messages sent to prospective students and hiring managers. Despite these efforts, the global pandemic depressed anticipated program participation by about a third. Still, a total of 396 students enrolled and 289 completed the courses and were accredited. The job applicant task achieved similar results (111 realized vs a 174 goal) but reported job placement was weaker at (9 realized vs. a 51 goal). The Cyberguardians program fills a critical void for cost-effective, royalty-free curriculum and training pertaining to DER technologies and cybersecurity that prospective energy workers must possess to be effective in the 21 st century. On this basis alone, the investment of taxpayer funds will pay dividends for years to come.

14 SOLAR ENERGY↗

DOE OSTI2024

Avian Activity Classification Using Recurrent Networks to Fuse Videos with Metadata on Imbalanced Datasets

Activity classification plays a crucial role in various real-life scenarios involving both humans and animals. There is an increasing need for precise activity classification focused on avian-solar interactions, as the usage of solar energy facilities, such as photovoltaic array power stations, has been observed to impact bird species richness, behavior, and activity. However, there has been no work to develop an automated system to monitor and classify these avian-solar interactions. All current methods rely on human observers, which is time and human resources costly and subject to errors related to searcher efficiency. With the recent success of Deep Learning models in activity classification problems, this paper develops a recurrent neural network-based model to automatically classify six avian activities around solar energy facilities. Our proposed model integrates critical feature engineering metadata with video frame data, enabling improved learning and more accurate activity classification. Furthermore, we address the challenge of data imbalance during training and demonstrate the efficacy of our model in detecting and classifying different activities within video tracks. Additionally, we analyze the saliency/backpropagation map of the trained proposed model and validate its decision-making rationale.

Avian activity classification; bidirectional LSTM;↗

NASA NTRS2009

Helios High Altitude Long Endurance Mission Mishap

This slide presentation reviews the failure of the Helios solar aircraft failure. Included are pictures of the aircraft, inflight, and after the mishap, analysis of the root causes of the mishap, contributing factors, recommendations and lessons learned in respect to crew training, and assessing the level of risk.

Henwood, Barton E.↗

DOE OSTI2023

Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling

The parameterization of key photosynthesis parameters is one of the key uncertain sources in modeling ecosystem gross primary productivity (GPP). Solar-induced chlorophyll fluorescence (SIF) offers a good proxy for GPP since it marks the actual process of photosynthesis; while machine learning (ML) provides a robust approach to model the GPP-SIF relationship. Here, we trained the boosted regressing tree (BRT) and the Random Forest ML models with Greenhouse Gases Observing Satellite SIF data and in situ GPP observations from 49 eddy covariance towers. These trained ML GPP-SIF models were fed into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to generate ELM-simulated global SIF estimates, which were then benchmarked against satellite SIF observations with a surrogate modeling approach. Our results indicated good modeling performance of the ML-based GPP-SIF relationship. The ELM model when fed with the ML GPP-SIF models also can well predict the spatial-temporal variations in SIF. We also found high model accuracy for the surrogate modeling. Model parameter sensitivity analysis suggested that the fraction of leaf nitrogen in RuBisCO (flnr) is the most sensitive parameter to the SIF; other sensitive parameters include the Ball-Berry stomatal conductance slope (mbbopt) and the vcmax entropy (vcmaxse). The posterior uncertainty in simulated GPP was greatly reduced after benchmarking, and the model produced improved spatial patterns of mean GPP relative to FLUXCOM GPP. Our integrated approach provides a new avenue for improving land models and using remote-sensing SIF, which can be further improved in the future with more ground- and satellite-based observations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

PV Inverter Experimental Dataset Version 2 with 100 Percent Power

The increase in power electronic based generation sources require accurate modeling of inverters. Accurate modeling requires experimental data over wider operation range. We used 20 kW off-the-shelf grid following PV inverter in the experiments. We used controllable AC supply and controllable DC supply to emulate AC and DC side characteristics. The experiments were performed at NREL's Energy Systems Integration Facility. The PV inverter is tested under 100%, 75%, 50%, 25% load conditions. In the first dataset, for each operating condition, controllable AC source voltage is varied from 0.88 to 1.09 per unit (p.u) with a step value of 0.025 p.u while keeping the frequency at 60 Hz. In the second dataset, under similar load conditions (100%, 75%, 50%, 25% ), the frequency of the controllable AC source voltage was varied from 59.4 Hz to 60.45 Hz with a step value of 0.2 Hz. Voltage and frequency range is chosen based on inverter protection. Voltages and currents on DC and AC side are included in the dataset.

14 SOLAR ENERGY↗

NASA NTRS2000

Genesis Trajectory Design

The Genesis mission will launch in 2001, sending the spacecraft into a halo orbit about the Sun-Earth L1 point to collect and return solar wind samples to the Earth for analysis in 2003. One of the most constraining aspects of the mission design is the requirement to return to the designated landing site (the Utah Test and Training Range, UTTR) during daylight hours. The ongoing mission design has led the development of a family of solutions that characterize a broad range of conditions at Earth entry. Characterizing this family provides insight into the possible existence of additional trajectories while also helping to narrow the search space by indicating where additional solutions are unlikely to exist; this contributes to a more efficient utilization of mission design resources.

Bell, Julia L.↗

NASA NTRS2003

Analysis of Wave and Particle Signatures Observed in Plasma Escape at Venus

Atmospheric gases escape from Venus as neutral and ionized atoms and molecules. Ion escape, considered here, occurs through ion pickup or collective plasma processes. The latter can arise from upward flow of nightside ionospheric plasma into the ionotail, day to night ionospheric flow into the ionotail, and scavenging of ionospheric plasma by ionosphere-magnetosheath instabilities at the ionopause. These plasma processes produce differing signatures in ion velocity and energy distributions and in ULF waves in the magnetic field. Using plasma ion spectra measured by the Pioneer Venus Orbiter (PVO) Orbiter Plasma Analyzer (OPA) and magnetic field fluctuations observed by the PVO Orbiter Magnetometer (OMAG) along with the expected particle and field signatures, various ion escape processes occurring along Pioneer Venus orbits are identified. In particular, OPA ion energy distributions are used in parallel with magnetic field power spectra and wave phase angles derived from OMAG measurements to study the characteristics of escaping ions. The principle ions observed escaping the influence of Venus are H+, He+ and 0'. In the ion energy distributions of the OPA, pickup ions appear hot relative to the much cooler ions flowing away from Venus in the ionotail and in the plasma clouds detached from the ionopause. This energy contrast is particularly evident downstream when PVO crosses the ionotail boundary from the hot solar wind plasma to the much cooler plasma within the tail. Magnetic field signatures accompanying the escaping ions appear as peaks in the power spectra at the corresponding ion cyclotron frequencies. Also, coherent wave trains at the same frequencies are observed in the phase angle plots of magnetic field fluctuations about the mean field.

Hartle, R. E.↗

DOE OSTI2022

Review of Wave Energy Converter Power Take-Off Systems, Testing Practices, and Evaluation Metrics: Preprint

While the field of wave energy has been the subject of numerical simulation, scale model testing, and precommercial project testing for decades, wave energy technologies remain in the early stages of development and must continuing proving themselves as a promising modern renewable energy field. One of the difficulties that wave energy systems have been struggling to overcome is the design of highly efficient energy conversion systems that can convert the mechanical power, derived from the oscillation of wave activated bodies, into another useful product. Often the power take-off (PTO) is defined as the single unit responsible for converting mechanical power into another usable form such as electricity, pressurized fluid, compressed air, and others. The PTO, and the entire power conversion chain (PCC), is of great importance as it affects not only how efficient wave power is converted into electricity, but also contributes to the mass, size, structural dynamics, and levelized cost of energy (LCOE) of the wave energy converter (WEC). Unlike wind and solar, there is no industrial standard device, or devices, for wave energy conversion and this diversity is transferred to the PTO system. The majority of current WEC PTO systems incorporate a mechanical or hydraulic drive train, power generator, and an electrical control system. The challenge of WEC PTO designs is designing a mechanical-to-electrical component that can efficiently convert irregular, bi-directional, low frequency and low alternating velocity wave motions. While gross average power levels can be predicted in advance, the variable wave elevation input has to be converted into smooth electrical output and hence usually necessitates some type of energy storage system, such as battery storage, accumulator super capacitors, etc., or other means of compensation such as an array of devices. One of the primary challenges for wave energy converter systems is the fluctuating nature of wave resources, which require WEC components to be designed to handle loads (i.e. torques, forces, and powers) that are many times greater than the average load. This approach requires a much greater PTO capacity than the average power output and indicates a higher cost. In addition, supporting mechanical coupling and or gearing can be added to the PCC to help alleviate the difficulties with transmission and control of fluctuating large loads with low frequencies (indicative of wave forcing) into smaller loads at higher frequencies (optimum for conventional electrical machine design) can quickly increase the complexity of the PCC which could result in a greater number of failure modes and increased maintenance costs. All of the previous points demonstrate how the PTO influences WEC dynamics, reliability, performance and cost which are critical design factors. This paper further explores these topics by providing a review of the state-of-the-art PTO systems currently under development, how these novel PTO systems are tested and derisked prior to precommercial deployment, and the evaluation metrics historically used to differentiate between PTO designs and how they can be improved to support control co-design focused development of wave energy systems.

laboratory testing↗

DOE OSTI2020

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.

14 SOLAR ENERGY↗

NASA NTRS1999

Photon-Counting Multikilohertz Microlaser Altimeters

Spaceborne laser altimeters in planetary orbit typically use high energy (approximately 100 mJ) solid state lasers, large telescopes (50 to 100 cm), and high detection thresholds to achieve unambiguous surface returns with few or no ifalse alarmsi resulting from solar background noise. As a result of this conservative design philosophy, spacecraft prime power weight, or size constraints typically restrict operations to modest repetition rates on the order of a few tens of Hz which, at a typical ground velocity of 7 Km/sec, limits along-track spatial sampling to one sample every few hundred meters. The surface return rate of an Earth-orbiting altimeter can be increased by up to two orders of magnitude for a given laser output power by emitting the available photons in a high frequency (few KHz) train of low energy (< 1 mJ) pulses, as produced by passively Q-switched solid state "microlasers", and using single photon detection. while simultaneously using much smaller telescope diameters on the order of 10 cm. Simple onboard software algorithms, based on post-detection Poisson filtering techniques, can be employed to identify and extract the surface data from solar background noise prior to onboard data storage or transmission to a ground station. Under NASAis Instrument Incubator Program, we have begun the design and construction of a scanning airborne instrument to demonstrate the potential advantages of the technique for future space missions.

Degnan, John J.↗

NASA NTRS1980

Research in the application of spectral data to crop identification and assessment, volume 2

The development of spectrometry crop development stage models is discussed with emphasis on models for corn and soybeans. One photothermal and four thermal meteorological models are evaluated. Spectral data were investigated as a source of information for crop yield models. Intercepted solar radiation and soil productivity are identified as factors related to yield which can be estimated from spectral data. Several techniques for machine classification of remotely sensed data for crop inventory were evaluated. Early season estimation, training procedures, the relationship of scene characteristics to classification performance, and full frame classification methods were studied. The optimal level for combining area and yield estimates of corn and soybeans is assessed utilizing current technology: digital analysis of LANDSAT MSS data on sample segments to provide area estimates and regression models to provide yield estimates.

Daughtry, C. S. T.↗

NASA NTRS1989

Geologic applications of Space Shuttle photography

Space Shuttle astronauts have used handheld cameras to take about 30,000 photographs of the earth as seen from orbit. These pictures provide valuable, true-color depictions of many geologically significant areas. While the photographs have areal coverages and resolutions similar to the more familiar Landsat MSS and TM images, they differ from the latter in having a wide variety of solar illumination angles and look angles. Astronaut photographs can be used as very small scale aerial photographs for geologic mapping and planning logistical support for field work. Astronaut photography offers unique opportunities, because of the intelligence and training of the on-orbit observer, for documenting dynamic geologic activity such as volcanic eruptions, dust storms, etc. Astronauts have photographed more than 3 dozen volcanic eruption plumes, some of which were not reported otherwise. The stereographic capability of astronaut photography also permits three-dimensional interpretation of geologic landforms which is commonly useful in analysis of structural geology. Astronauts have also photographed about 20 known impact craters as part of project to discover presently unknown examples in Africa, South America, and Australia.

Wood, Charles A.↗